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Team Collaboration Networks and Multidisciplinarity in Diabetes Care: Implications for Patient Outcomes
Abstract
Prevalence of type 2 diabetes mellitus (T2DM) has almost doubled in recent decades and commonly presents comorbidities and complications. T2DM is a multisystemic disease, requiring multidisciplinary treatment provided by teams working in a coordinated and collaborative manner. The application of Social Network Analysis techniques in the healthcare domain has allowed researchers to analyze interaction between professionals and their roles inside care teams. We studied whether the structure of care teams, modeled as complex social networks, is associated with patient progression. For this, we illustrate a data-driven methodology and use existing social network analysis metrics and metrics proposed for this research. We analyzed appointment and HbA1c blood test result data from patients treated at three primary health care centers, representing six different practices. Patients with good metabolic control during the analyzed period were treated by teams that were more interactive, collaborative and multidisciplinary, whereas patients with worsening or unstable metabolic control were treated by teams with less collaboration and more continuity breakdowns. Results from the proposed metrics were consistent with previous literature and reveal relevant aspects of collaboration and multidisciplinarity.
... Regarding diabetes, work has already been conducted on proposing collaboration metrics for social network analysis [39] and analyzing patterns of collaboration with regard to multiple disciplines [40]. Accordingly, that study found that more integrated care in which doctors, nurses, and nutritionists participate equally is positively associated with patient evolution. ...
... [40]), or have focused on metrics (e.g. [39]), while this study aims to find collaboration patterns between treating healthcare professionals, with a special focus on patient adherence. Therefore, the guiding questions for this research are: Is it possible to identify collaboration patterns between healthcare professionals in diabetes treatment? ...
... Previous research (e.g. [24,39,40]) uses HbA1c test results to group patients according to whether they achieved the goals proposed by the American Diabetes Association. In these studies, a patient who "improves" is one who manages to reduce their HbA1c below 7%. ...
Type 2 Diabetes Mellitus (T2DM) is a chronic disease that has been increasing in prevalence in recent years and that can cause severe complications. To ensure patient care is administered correctly, it is necessary for medical treatment teams to be both multidisciplinary and cohesive. The analysis of health processes is a constant challenge due to their high variability and complexity. This paper proposes a method based on the analysis of social networks to detect treatment networks, and to identify a relationship between these networks and patient evolution, as measured by glycated hemoglobin (HbA1c) levels. The networks were segmented based on patient adherence to their medical appointments and their mean time of delay. We applied this method on a sample of 1,574 patients diagnosed with T2DM. Results show that participatory treatment -in which a patient sees a particular group of professionals on a recurrent basis - together with high levels of adherence are associated to those patients who improve their HbA1c levels in the case of high levels of adherence, while those who continually experience referrals to different professionals, remain unstable and, in some cases, get worse. On the other hand, in order to maintain a patient as stable, it is enough with a continuous control of the patient, regardless of the recurrence to the same professionals.
... They used a 0.05 threshold of significance and six classification algorithms: Hoeffding Tree, Random Forest, JRip, Multilayer Perceptron (deep learning algorithm), Bayes Network, and Multilayer Perceptron (deep learning algorithm). Precision, recall, F-measure, and ROC area were the metrics utilized to evaluate the results [27,28]. ...
... However, it tends to allow for less productive exchanges and communications and hinders teamwork which affects outcomes [46]. The network structure on the other end, consists of interactions between equal entities and tends to be more conducive to collaboration which leads to better outcomes [47,48]. Accordingly, the literature indicates that the effectiveness of collaborations is strongly tied with the research team's power dynamics [49]. ...
Background
Collaboration between biomedical research and community-based primary health care actors is essential to translate evidence into clinical practice. However, little is known about the characteristics and impacts of implementing collaborative models. Thus, we sought to identify and describe collaboration models that bridge biomedical research and community-based primary health care in chronic disease management.
Methods
We conducted a scoping review using Medline, Embase, Web of Science, and Cochrane Library from inception to November 2020, to identify studies describing or evaluating collaboration models. We also searched grey literature, screened reference lists, and contacted experts to retrieve further relevant references. The list of studies was then refined using more specific inclusion and exclusion criteria. Two reviewers independently selected studies and extracted relevant data (characteristics of studies, participants, collaborations, and outcomes). No bias assessment was performed. A panel of experts in the field was consulted to interpret the data. Results were presented with descriptive statistics and narrative synthesis.
Results
Thirteen studies presenting 20 unique collaboration models were included. These studies were conducted in North America ( n = 7), Europe ( n = 5) and Asia ( n = 1). Collaborations were implemented between 1967 and 2014. They involved a variety of profiles including biomedical researchers ( n = 20); community-based primary health care actors ( n = 20); clinical researchers ( n = 15); medical specialists ( n = 6); and patients, citizens, or users ( n = 5). The main clinical focus was cardiovascular disease ( n = 8). Almost half of the collaborations operated at an international level ( n = 9) and the majority adopted either a network ( n = 7) or hierarchical structure ( n = 6). We identified significant implementation barriers (lack of knowledge, financial support, and robust management structure) and collaboration facilitators (partnership, cooperation, multidisciplinary research teams). Out of the 20 included collaboration models, seven reported measurable impact.
Conclusion
We identified a large variety of collaboration models representing several clinical and research profiles and fields of expertise. As they are all based in high-income countries, further research should aim to identify collaborations in low-income countries, to determine which models and/or characteristics, could better translate evidence into clinical practice in these contexts.
... In this work, the binary classifier can be used to predict the glucose level. Saint-Pierre et al. [11] proposed a diabetic care by a collaboration team. This work analysis the team's role in the continuity of diabetic care. ...
The integration of the Internet of Things (IoT) and cloud computing is the most popular growing technology in the IT world. IoT integrated cloud computing technology can be used in smart cities, health care, smart homes, environmental monitoring, etc. In recent days, IoT integrated cloud can be used in the health care system for remote patient care, emergency care, disease prediction, pharmacy management, etc. but, still, security of patient data and disease prediction accuracy is a major concern. Numerous machine learning approaches were used for effective early disease prediction. However, machine learning takes more time and less performance while classification. In this research work, the Attribute based Searchable Honey Encryption with Functional Neural Network (ABSHE-FNN) framework is proposed to analyze the disease and provide stronger security in IoT-cloud healthcare data. In this work, the Cardiovascular Disease and Pima Indians diabetes dataset are used for heart and diabetic disease classification. Initially, means-mode normalization removes the noise and normalizes the IoT data, which helps to enhance the quality of data. Rectified Linear Unit (RLU) was applied to adjust the feature weight to reduce the training cost and error classification. This proposed ABSHE-FNN technique provides better security and achieves 92.79% disease classification accuracy compared to existing techniques.
... T2DM represents over 90% of patients with diabetes and prompts microvascular and full scale vascular inconveniences that cause significant mental and actual misery to the two patients and guardians, bringing about a gigantic weight on the medical services framework [3]. The range of complications arising from diabetes, due to the damaging nature of glucose molecules on the microand macro-vascular system includes: cardiovascular disease, coronary heart disease, blindness, nephropathy, peripheral neural disease, amputations, depression and erectile dysfunction [4,5]. Despite increasing knowledge regarding risk factors for type 2 diabetes and evidence for successful prevention programs, the incidence and prevalence of the disease continues to rise globally. ...
In 2050, the world's diabetic patients will arrive at 642 million, which implies that one of the ten grown-ups later on is experiencing diabetes. Diabetes mellitus (DM) is characterized as a gathering of metabolic issues applying critical tension on human wellbeing around the world. DM is a persistent sickness portrayed by hyperglycemia and it might cause numerous inconveniences. To forestall this issue, to break down the given medical clinic dataset by directed AI technique(SMLT) with catch a few data resembles, variable ID, uni-variate examination, bi-variate and multi-variate investigation, missing worth therapies and dissect the information approval, information cleaning/getting ready and information perception will be done on the whole given dataset. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in prediction of diabetic patients by given attributes of dataset with evaluation of GUI based user interface diabetes attribute prediction. Additionally, it observes to lead an increase the highest accuracy in diabetic prediction of attributes by a significantly better classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and F1 Score.
... Therefore, if people are informed about this information, they may be increasingly interested in a healthy way of life, since they all want to live more and better. [25], [26] Question 3: In your daily life, which of the "Eight Natural Remedies", presented at the meetings, do you think you can use to improve your health? ...
Objective: to identify the social representations of type 2 Diabetes Mellitus patients on the effect of an educational intervention to guide the adoption of healthy habits. Method: This was a qualitive study, where semi-structured intervies. Semi-structured interviews were applied after an educational intervention and were analized using the Collective Subject Discourse technique, based on the Theory of Social Representations. Results: 21 patients participated. In the analysis of the three questions, eleven Central Ideas emerged: “Aspects that contribute to health”; "There are difficulties for putting it into practice"; "Everything positive, including the attention received"; "It is possible, everything is there in nature"; "It is possible, but it does not replace the drugs"; "It is not always possible, it depends on some factors"; “If the person doesn't want it, everything is difficult”; “You can use them all”; “Mainly sun, physical activity, healthy eating ”; "I like water very much"; “Trust in God is essential”. Final considerations: the participants recognized the importance of adopting a healthy lifestyle and highlighted the positive results of adopting healthy habits.
Purpose:
Interorganizational collaborations are crucial for delivering high-quality, integrated healthcare services. To maximize the benefits of these collaborative networks, effective governance structures and mechanisms must be in place. While previous studies have extensively examined organizational-level factors, such as partner capabilities and backgrounds, this study focuses on network-level factors, including collaboration structures and tie characteristics that shape effective network governance.
Design/methodology/approach:
A systematic literature review (SLR) was conducted to identify and synthesize the key network-level factors influencing governance structures and mechanisms in healthcare networks.
Findings:
The review identified 22 critical factors, categorized into three primary groups that impact network governance. These findings offer a robust foundation for developing context-sensitive governance models tailored to healthcare systems.
Practical implications:
This study provides valuable insights for healthcare practitioners, policymakers and researchers by highlighting key factors that can improve interorganizational collaboration within healthcare systems. The findings contribute to both theory and practice, with the potential to enhance healthcare service delivery and patient outcomes.
Originality/value:
This study is the first to systematically identify and categorize the network-level factors that influence governance structures and mechanisms in healthcare networks, providing a comprehensive and novel contribution to the field.
This review examines the epidemiological trends, pathophysiologic mechanisms, and current and future therapeutic strategies for diabetic retinopathy (DR), focusing on innovative management countermeasures in the face of this global public health challenge. As the number of patients with diabetes continues to increase, DR, as one of its major complications, poses a significant threat to global visual health. This review not only summarises the latest advances in personalised treatment and emerging therapeutic modalities (such as anti-vascular endothelial growth factor therapy, laser treatment, surgical procedures and cutting-edge gene and stem cell therapies) but also emphasises the revolutionary potential of telemedicine technologies and digital health platforms to improve DR screening and adherence among people with diabetes. We show how these technological innovations, especially in resource-limited settings, can achieve early diagnosis and effective treatment, thereby significantly reducing the public health burden of DR. In addition, this article highlights the critical role of interdisciplinary teamwork in optimising the comprehensive management of DR, involving close collaboration among physicians, researchers, patient education specialists and policy-makers, as well as the importance of implementing these innovative solutions through societal engagement and policy support. By highlighting these innovative strategies and their specific impact on improving public health practices, this review offers new perspectives and strategies for the future management of DR, with the goal of promoting the prevention, diagnosis and treatment of DR worldwide, improving patient prognosis and enhancing quality of life.
Background
Providing efficient and targeted services for patients with mental health problems requires efficient collaboration and coordination within healthcare providers, but measuring collaboration using traditional methods is challenging.
Aims
To explore the patient-sharing networks of professionals taking care of different groups of patients with mental or substance use disorders.
Method
We used data that covered adult patients’ visits to the primary care service providers of seven municipalities in Finland during year 2021. Data included 8,217 patients (147,430 visits) with mental or substance use disorders who were treated by 1,566 health care professionals. We calculated descriptive network metrics to examine the connectivity of professionals in three different patient groups (patients with substance use disorders, psychotic disorders, and depressive disorders) and compared these characteristics to a network based on all patients. We also analyzed whether patient sharing was associated with the health care professionals’ attributes (occupational group, municipality) using Exponential Random Graph Models (ERGM).
Results
Diagnosis-specific networks were denser and more connected compared to the all-patients network. Nurses were the most central occupation in all the diagnosis-specific networks and especially in the substance use disorder patients network. When examining all patients, two professionals were more likely to share patients when they belonged to the same occupational group. However, in the network with depressive disorder patients we found the opposite: professionals were more likely to share patients if they were of different occupational groups.
Conclusions
Patient-sharing networks within patients with a specific mental or substance use disorders are denser and more connected than networks based on all patients with mental or substance use disorders. In the substance use disorder patients network particularly, nurses were the most central occupation. Multi-professional connections were more likely in depressive disorder networks than in the all-patients network.
In the field of clinical conclusion, Machine learning (ML) strategies are broadly taken on for expectation and grouping tasks. The point of ML strategies is to arrange the illness all the more precisely in a proficient way for the determination of sickness. There is steady development in tolerant life care machines and frameworks. Thus, this development builds the typical existence of individuals. Be that as it may, these medical services frameworks face a few difficulties and issues like deluding patients’ data, security of information, absence of exact information, absence of medico data, classifiers for expectation, and some more. The point of this study is to propose a model in view of ML to determine patients to have diabetes and coronary illness in brilliant clinics. In this sense, it was underlined that by the portrayal for the job of ML models is important to advances in shrewd clinic climate. The exact pace of the conclusion (order) in view of research center discoveries can be improved through light ML models. Three ML models, in particular, support vector machines (SVM), Decision Tree (DT), and Gradient Boosting (GB), will prepare and test based on lab datasets. Three primary systemic situations of diabetes and coronary illness analyzed, for example, in light of unique and standardized datasets and those in view of component choice, were introduced. The proposed model in view of ML can be filled in as a clinical choice emotionally supportive network.KeywordsSmart healthcare systemsMachine learningDiabetes diseaseHeart disease
In today’s highly developed, massive corporate offices or industries, it is difficult to personally evaluate every employee’s performance and recommend them for promotion. It has only been the subject of a few research projects, but those who have worked on it have created algorithms for predicting promotions as well as the fundamental traits and job qualities for each person. The incorporation of extra attributes allows our model to do more with fewer strategies. The goal of this research is to provide a machine learning-based system for predicting whether or not an employee will be promoted. We do this by offering a linear model that offers a respectable level of accuracy at a cheap cost. This process takes into account the training record, annual performance review score, length of service, key performance indicators, and other elements of the employee. Due to the limited number of classifications, a linear classifier was utilized to train the model. This linear classifier completes 50 iterations with an accuracy rate of 92.6%. Using this method, you may be able to acquire an answer on the likelihood of promoting any employee. This software might prove to be the saving grace for an organization’s HR department.KeywordsMLEpochsLinear classifierKey performance indicatorsAccuracy
Multidisciplinary treatment and continuity of care throughout treatment are important for ensuring metabolic control and avoiding complications in diabetic patients. This study examines the relationship between continuity of care of the treating disciplines and clinical evolution of patients. Data from 1836 adult patients experiencing type 2 diabetes mellitus were analyzed, in a period between 12 and 24 months. Continuity was measured by using four well known indices: Usual Provider Continuity (UPC), Continuity of Care Index (COCI), Herfindahl Index (HI), and Sequential Continuity (SECON). Patients were divided into five segments according to metabolic control: well-controlled, worsened, moderately decompensated, highly decompensated, and improved. Well-controlled patients had higher continuity by physicians according to UPC and HI indices (p-values 0.029 and <0.003), whereas highly decompensated patients had less continuity in HI (p-value 0.020). Continuity for nurses was similar, with a greater continuity among well-controlled patients (p-values 0.015 and 0.001 for UPC and HI indices), and less among highly decompensated patients (p-values 0.004 and <0.001 for UPC and HI indices). Improved patients had greater adherence to the protocol than those who worsened. The SECON index showed no significant differences across the disciplines. This study identified a relationship between physicians and nurse’s continuity of care and metabolic control in patients with diabetes, consistent with qualitative findings that highlight the role of nurses in treatment.
The collaboration among physicians during episodes of care for hospitalised patients makes a significant contribution to effective health outcomes. Although physician collaborations are frequently analysed to explore their impact on healthcare outcomes, the impact of the grouping structure of such collaborations is still unknown. The main purpose of this study is to improve health outcomes by analysing the attributes of patient-sharing physician collaboration networks. This study explores the impact of different attributes of patient-sharing physician collaboration networks (PCNs) on hospitalisation cost, length of stay and readmission rate. We use an electronic health insurance claim dataset to construct and explore PCNs. Each PCN is categorised as either ‘low’ or ‘high’ in terms of hospitalisation cost, length of stay and readmission rate. Isomorphic classes of triad census, and clique and clan concepts of subgroup analysis are used to analyse PCNs. The results show that the clique and clan of physician collaborations affect only hospitalisation cost and length of stay. Two isomorphism classes of triad census (i.e., closed triad and open triad) impact hospitalisation cost, length of stay and readmission rate. Physician collaborations in larger groups, instead of smaller groups, is related to lower hospitalisation cost and shorter length of stay. The findings and insights from this research can potentially help healthcare stakeholders to formulate better policies, which will eventually improve the quality of care while reducing cost.
Type 2 Diabetes Mellitus (T2DM) is a chronic disease that has risen in prominence in recent years and can cause serious complications. Several studies show that the level of adherence to different types of treatment has a direct correlation with the positive evolution of chronic diseases. While such studies relate to patient adherence to medication, those that concern adherence to medical appointments do not distinguish between the different disciplines that attend to or refer patients. This study analyses the relationship between adherence to referrals made by three distinct disciplines (doctors, nurses, and nutritionists) and the results of HbA1c tests from a sample of 2290 patients with T2DM. The aim is to determine whether a relationship exists between patient improvement and the frequency with which they attend scheduled appointments in a timely manner, having been previously referred from or to a particular discipline. Results showed that patients tended to be more adherent when their next appointment is with a doctor, and less adherent when it is with a nurse or nutritionist. Furthermore, patients that remained stable had higher rates of adherence, whereas those with lower adherence tended to be more decompensated. The results can enable healthcare professionals to monitor patients and place particular emphasis on those who do not attend their scheduled appointments in a timely manner.
Objective
Continuity of care is a long-standing feature of healthcare, especially of general practice. It is associated with increased patient satisfaction, increased take-up of health promotion, greater adherence to medical advice and decreased use of hospital services. This review aims to examine whether there is a relationship between the receipt of continuity of doctor care and mortality.
Design
Systematic review without meta-analysis.
Data sources
MEDLINE, Embase and the Web of Science, from 1996 to 2017.
Eligibility criteria for selecting studies
Peer-reviewed primary research articles, published in English which reported measured continuity of care received by patients from any kind of doctor, in any setting, in any country, related to measured mortality of those patients.
Results
Of the 726 articles identified in searches, 22 fulfilled the eligibility criteria. The studies were all cohort or cross-sectional and most adjusted for multiple potential confounding factors. These studies came from nine countries with very different cultures and health systems. We found such heterogeneity of continuity and mortality measurement methods and time frames that it was not possible to combine the results of studies. However, 18 (81.8%) high-quality studies reported statistically significant reductions in mortality, with increased continuity of care. 16 of these were with all-cause mortality. Three others showed no association and one demonstrated mixed results. These significant protective effects occurred with both generalist and specialist doctors.
Conclusions
This first systematic review reveals that increased continuity of care by doctors is associated with lower mortality rates. Although all the evidence is observational, patients across cultural boundaries appear to benefit from continuity of care with both generalist and specialist doctors. Many of these articles called for continuity to be given a higher priority in healthcare planning. Despite substantial, successive, technical advances in medicine, interpersonal factors remain important.
PROSPERO registration number
CRD42016042091.
Background:
Public health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes.
Objective:
Health care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients.
Methods:
This paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise.
Results:
The method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner.
Conclusions:
By discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.
Background
Type 2 Diabetes Mellitus (T2DM) is reported to affect one in 11 adults worldwide, with over 80% of T2DM patients residing in low-to-middle-income countries. Health systems play an integral role in responding to this increasing global prevalence, and are key to ensuring effective diabetes management. We conducted a systematic review to examine the health system-level factors influencing T2DM awareness, treatment, adherence, and control.
Methods and findings
A protocol for this study was published on the PROSPERO international prospective register of systematic reviews (PROSPERO 2016: CRD42016048185). Studies included in this review reported the effects of health systems factors, interventions, policies, or programmes on T2DM control, awareness, treatment, and adherence. The following databases were searched on 22 February 2017: Medline, Embase, Global health, LILACS, Africa-Wide, IMSEAR, IMEMR, and WPRIM. There were no restrictions on date, language, or study designs. Two reviewers independently screened studies for eligibility, extracted the data, and screened for risk of bias. Thereafter, we performed a narrative synthesis. A meta-analysis was not conducted due to methodological heterogeneity across different aspects of included studies. 93 studies were included for qualitative synthesis; 7 were conducted in LMICs. Through this review, we found two key health system barriers to effective T2DM care and management: financial constraints faced by the patient and limited access to health services and medication. We also found three health system factors that facilitate effective T2DM care and management: the use of innovative care models, increased pharmacist involvement in care delivery, and education programmes led by healthcare professionals.
Conclusions
This review points to the importance of reducing, or possibly eliminating, out-of-pocket costs for diabetes medication and self-monitoring supplies. It also points to the potential of adopting more innovative and integrated models of care, and the value of task-sharing of care with pharmacists. More studies which identify the effect of health system arrangements on various outcomes, particularly awareness, are needed.
Purpose
Effective management for type 2 diabetes mellitus (DM) can slow the progression of kidney outcomes and reduce hospital admissions. Better continuity of care (COC) was found to improve patients’ adherence and self-management. This study examined the associations between COC, hospitalization, and end-stage renal disease (ESRD) in DM patients.
Patients and methods
In the cohort study, data from 1996 to 2012 were retrieved from the Longitudinal Health Insurance Database, using inverse probability weighted analysis. A total of 26,063 patients with newly diagnosed type 2 DM who had been treated with antihyperglycemic agents were included. COC is to assess the extent to which a DM patient visited the same physician during the study period. This study categorized COC into 3 groups – low, intermediate, and high, – according to the distribution of scores in our sample.
Results
The number of ESRD patients in the high, intermediate, and low COC groups were 92 (22.33%), 130 (31.55%), and 190 (46.12%), respectively, and the mean follow-up periods for the 3 groups were 7.13, 7.12, and 7.27 years, respectively. After using inverse probability weighting, the intermediate and low COC groups were significantly associated with an increased risk of ESRD compared with the high COC group (adjusted hazard ratio (aHR) 1.36 [95% CI, 1.03–1.80] and aHR 1.76 [95% CI, 1.35–2.30], respectively). The intermediate and low COC groups were also significantly associated with the subsequent hospitalization compared with the high COC group (aHR 1.15 [95% CI, 0.99–1.33] and aHR 1.72 [95% CI, 1.50–1.97], respectively).
Conclusion
COC is related to ESRD onset and subsequent hospitalization among patients with DM. This study suggested that when DM patients keep visiting the same physician for managing their diseases, the progression of renal disease can be prevented.
OBJECTIVE To evaluate the 5-year effectiveness of a multidisciplinary Risk Assessment and Management Programme–Diabetes Mellitus (RAMP-DM) in primary care patients with type 2 diabetes.
RESEARCH DESIGN AND METHODS A 5-year prospective cohort study was conducted with 121,584 Chinese primary care patients with type 2 DM who were recruited between August 2009 and June 2011. Missing data were dealt with multiple imputations. After excluding patients with prior diabetes mellitus (DM)-related complications and one-to-one propensity score matching on all patient characteristics, 26,718 RAMP-DM participants and 26,718 matched usual care patients were followed up for a median time of 4.5 years. The effect of RAMP-DM on nine DM-related complications and all-cause mortality were evaluated using Cox regressions. The first incidence for each event was used for all models. Health service use was analyzed using negative binomial regressions. Subgroup analyses on different patient characteristics were performed.
RESULTS The cumulative incidence of all events (DM-related complications and all-cause mortality) was 23.2% in the RAMP-DM group and 43.6% in the usual care group. RAMP-DM led to significantly greater reductions in cardiovascular disease (CVD) risk by 56.6% (95% CI 54.5, 58.6), microvascular complications by 11.9% (95% CI 7.0, 16.6), mortality by 66.1% (95% CI 64.3, 67.9), specialist attendance by 35.0% (95% CI 33.6, 36.4), emergency attendance by 41.2% (95% CI 39.8, 42.5), and hospitalizations by 58.5% (95% CI 57.2, 59.7). Patients with low baseline CVD risks benefitted the most from RAMP-DM, which decreased CVD and mortality risk by 60.4% (95% CI 51.8, 67.5) and 83.6% (95% CI 79.3, 87.0), respectively.
CONCLUSIONS This naturalistic study highlighted the importance of early optimal DM control and risk factor management by risk stratification and multidisciplinary, protocol-driven, chronic disease model care to delay disease progression and prevent complications.
Background and Objectives: While social network analysis has left a remarkable practical impact in the health-care field, the potential implication of this methodology in the primary health domain is poorly researched. Hence, this study aimed to explore the use and usefulness social network analysis in the context of primary health care.
Introduction: In this study we investigate whether clinic level continuity of care (COC) for individuals with chronic obstructive pulmonary disease (COPD) is associated with better health care outcomes and lower costs in a Swedish setting.
Methods: Individuals with COPD (N = 20,187) were identified through ICD-10 codes in all Stockholm County health care registries in 2007–2011 (59% female, 40% in the age group 65–74 years). We followed the individuals prospectively for 365 days after their first outpatient visit in 2012. Individual associations between COC and incidence of any hospitalization or emergency department visit and total costs for health care and pharmaceuticals were quantified by regression analysis, controlling for age, sex, comorbidity and number of visits. Clinic level COC was measured through the Bice–Boxerman COC index, grouped into quintiles.
Results: At baseline, 26% of the individuals had been hospitalized at least once and 73% had dispensed at least seven prescription drugs (23% at least 16) in the last year. Patients in the lowest COC quintile (Q1) had higher probabilities of any hospitalization and any emergency department visit compared to those in Q5 (odds ratio 2.17 [95% CI 1.95–2.43] and 2.06 [1.86–2.28], respectively). Patients in Q1 also on average had 58% [95% CI: 52–64] higher costs.
Conclusion: The findings show robust associations between clinic level COC and outcomes. These results verify the importance of COC, and suggest that clinic level COC is of relevance to both better outcomes for COPD patients and more efficient use of resources.
Background:
Specialised diabetes teams, specifically certified nurse and dietitian diabetes educator teams, are being integrated part-time into primary care to provide better care and support for Canadians living with diabetes. This practice model is being implemented throughout Canada in an effort to increase patient access to diabetes education, self-management training, and support. Interprofessional collaboration can have positive effects on both health processes and patient health outcomes, but few studies have explored how health professionals are introduced to and transition into this kind of interprofessional work.
Method:
Data from 18 interviews with diabetes educators, 16 primary care physicians, 23 educators' reflective journals, and 10 quarterly debriefing sessions were coded and analysed using a directed content analysis approach, facilitated by NVIVO software.
Results:
Four major themes emerged related to challenges faced, strategies adopted, and benefits observed during this transition into interprofessional collaboration between diabetes educators and primary care physicians: (a) negotiating space, place, and role; (b) fostering working relationships; (c) performing collectively; and (d) enhancing knowledge exchange.
Conclusions:
Our findings provide insight into how healthcare professionals who have not traditionally worked together in primary care are collaborating to integrate health services essential for diabetes management. Based on the experiences and personal reflections of participants, establishing new ways of working requires negotiating space and place to practice, role clarification, and frequent and effective modes of formal and informal communication to nurture the development of trust and mutual respect, which are vital to success.
Continuity of care is an important quality outcome of patient care. This study aimed to investigate the relationship between personal continuity and blood pressure (BP) control among the patients with hypertension in an academic primary care centre. Between January and May 2012, we conducted a retrospective review of medical records of patients with hypertension who had been followed up for at least 1 year in the Primary Care Clinic, University of Malaya Medical Centre, Malaysia. In this setting, doctors who provided care for hypertension included postgraduate family medicine trainees, non-trainee doctors and academic staff. Systematic random sampling (1:4) was used for patient selection. BP control was defined as less than 130/80 mm Hg for patients with diabetes mellitus, proteinuria and chronic kidney disease and less than 140/90 mm Hg for all other patients. Continuity of care was assessed using the usual provider continuity index (UPCI), which is the ratio of patient visits to the usual provider to the total number of visits to all providers in 1 year. A UPC index of zero denotes no continuity while an index of one reflects perfect continuity with only the usual provider. We reviewed a total of 1060 medical records. The patients' mean age was 62.0 years (SD 10.4). The majority was women (59.2%) and married (85.7%). The mean number of visits in a year was 3.85 (SD 1.36). A total of 72 doctors had provided consultations (55 postgraduate family medicine trainees, 8 non-trainee doctors and 9 academic staff). The mean UPCI was 0.43 (SD 0.34). Target BP was achieved in 42% of the patients. There was no significant relationship between BP control and personal continuity after adjustment for total number of visits. Continuity of care was not associated with BP control in our centre. Further studies are needed to explore the reasons for this.
Research on collaboration in primary care focuses on specific diseases or types of collaboration. We investigate the effects of such collaboration by bringing together the results of scientific studies.
We conducted a systematic literature review of PubMed, CINAHL, Cochrane and EMBASE. The review was restricted to publications that test outcomes of multidisciplinary collaboration in primary care in high-income countries. A conceptual model is used to structure the analysis.
Fifty-one studies comply with the selection criteria about collaboration in primary care. Approximately half of the 139 outcomes in these studies is non-significant. Studies among older patients, in particular, report non-significant outcomes (p < .05). By contrast, a higher proportion of significant results were found in studies that report on clinical outcomes.
This review shows a large diversity in the types of collaboration in primary care; and also thus a large proportion of outcomes do not seem to be positively affected by collaboration. Both the characteristics of the structure of the collaboration and the collaboration processes themselves affect the outcomes. More research is necessary to understand the mechanism behind the success of collaboration, especially on the exact nature of collaboration and the context in which collaboration takes place.
Cardiovascular disease is the leading cause of mortality and morbidity in the United States. Primary care teams can be best suited to improve quality of care and lower costs for patients with cardiovascular disease. This study evaluates the associations between primary care team communication, interaction, and coordination (ie, social networks); quality of care; and costs for patients with cardiovascular disease.
Using a sociometric survey, 155 health professionals from 31 teams at 6 primary care clinics identified with whom they interact daily about patient care. Social network analysis calculated variables of density and centralization representing team interaction structures. Three-level hierarchical modeling evaluated the link between team network density, centralization, and number of patients with a diagnosis of cardiovascular disease for controlled blood pressure and cholesterol, counts of urgent care visits, emergency department visits, hospital days, and medical care costs in the previous 12 months.
Teams with dense interactions among all team members were associated with fewer hospital days (rate ratio [RR] = 0.62; 95% CI, 0.50-0.77) and lower medical care costs (-781 to -506; 95% CI, 810). Team-shared vision about goals and expectations mediated the relationship between social network structures and patient quality of care outcomes.
Primary care teams that are more interconnected and less centralized and that have a shared team vision are better positioned to deliver high-quality cardiovascular disease care at a lower cost.
© 2015 Annals of Family Medicine, Inc.
Few studies have examined interprofessional practice (IPP) from a mental health service perspective. This study applied a mixed-method approach to examine the IPP and learning occurring in a youth mental health service in Tasmania, Australia. The aims of the study were to investigate the extent to which staff were networked, how collaboratively they practiced and supported student learning and to elicit the organisation’s strengths and opportunities regarding IPP and learning. Six data sets were collected: pre- and post-test readiness for interprofessional learning surveys, Social Network survey, organisational readiness for IPP and learning checklist, “talking wall” role clarification activity and observations of participants working through a clinical case study. Participants (n = 19) were well-networked and demonstrated a patient-centred approach. Results confirmed participants’ positive attitudes to IPP and learning and identified ways to strengthen the organisation’s interprofessional capability. This mixed-method approach could assist others to investigate IPP and learning.
To assess the change in level of diabetes quality management in primary care groups and outpatient clinics after feedback and tailored support.
This before-and-after study with a 1-year follow-up surveyed quality managers on six domains of quality management. Questionnaires measured organization of care, multidisciplinary teamwork, patient centeredness, performance results, quality improvement policy, and management strategies (score range 0-100%). Based on the scores, responders received feedback and a benchmark and were granted access to a toolbox of quality improvement instruments. If requested, additional support in improving quality management was available, consisting of an elucidating phone call or a visit from an experienced consultant. After 1 year, the level of quality management was measured again.
Of the initially 60 participating care groups, 51 completed the study. The total quality management score improved from 59.8% (95% CI 57.0-62.6%) to 65.1% (62.8-67.5%; P < 0.0001). The same applied to all six domains. The feedback and benchmark improved the total quality management score (P = 0.001). Of the 44 participating outpatient clinics, 28 completed the study. Their total score changed from 65.7% (CI 60.3-71.1%) to 67.3% (CI 62.9-71.7%; P = 0.30). Only the results in the domain multidisciplinary teamwork improved (P = 0.001).
Measuring quality management and providing feedback and a benchmark improves the level of quality management in care groups but not in outpatient clinics. The questionnaires might also be a useful asset for other diabetes care groups, such as Accountable Care Organizations.
© 2014 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.
INTRODUCTION: Hyperglycemia is one of the most frequent metabolic complications in hospitalized patients. Increased risk of infection following hyperglycemia has been reported in hospitalized patients and infections may also cause insulin resistance which complicates the control of blood glucose level. In this study the impact of the clinical pharmacist interventions on the glycemic control in patients admitted to infectious diseases ward has been evaluated. METHODOLOGY: We conducted a prospective, pre-post interventional study among patients with hyperglycemia. The clinical pharmacist-led multidisciplinary team managed the glycemic profile of patients according to an established insulin protocol commonly used in internal wards. Clinical pharmacists reviewed patients' medical charts for proper insulin administration, evaluated nurses' technique for insulin injection and blood glucose measurement, and educated patients about symptoms of hypoglycemia and the importance of adherence to different aspects of their glycemic management. RESULTS: The percentage of controlled random blood sugar increased from 13.8% in the pre-intervention to 22.3% in the post-intervention group (p value < 0.01). On the other hand, the percentage of controlled fasting blood sugars in the post-intervention group was non-significantly higher than in the pre-intervention group. CONCLUSION: Pharmacists and additional health care providers from other departments such as nursing and dietary departments need to be devoted to glycemic control service. Collaborative practice agreement between physicians is necessary to promote this service and help to increase the use of such services in different settings for diabetes control.
Hyperglycemia is one of the most frequent metabolic complications in hospitalized patients. Increased risk of infection following hyperglycemia has been reported in hospitalized patients and infections may also cause insulin resistance which complicates the control of blood glucose level. In this study the impact of the clinical pharmacist interventions on the glycemic control in patients admitted to infectious diseases ward has been evaluated.
We conducted a prospective, pre-post interventional study among patients with hyperglycemia. The clinical pharmacist-led multidisciplinary team managed the glycemic profile of patients according to an established insulin protocol commonly used in internal wards. Clinical pharmacists reviewed patients' medical charts for proper insulin administration, evaluated nurses' technique for insulin injection and blood glucose measurement, and educated patients about symptoms of hypoglycemia and the importance of adherence to different aspects of their glycemic management.
The percentage of controlled random blood sugar increased from 13.8% in the pre-intervention to 22.3% in the post-intervention group (p value < 0.01). On the other hand, the percentage of controlled fasting blood sugars in the post-intervention group was non-significantly higher than in the pre-intervention group.
Pharmacists and additional health care providers from other departments such as nursing and dietary departments need to be devoted to glycemic control service. Collaborative practice agreement between physicians is necessary to promote this service and help to increase the use of such services in different settings for diabetes control.
Type 1 diabetes is one of the most frequent long-term endocrine childhood disorders and the Swedish National Diabetes Register for children states that adolescents (12-18 years) constitute the most vulnerable patient group in terms of metabolic control. The aim of this study was to examine how a multidisciplinary team functions when caring for adolescents with type 1 diabetes.
Qualitative interviews were performed with 17 health professionals at a Paediatric Diabetes Care Unit in a Swedish university hospital. The interviews were analysed to gain insight into a multidisciplinary care team's experiences of various organizational processes and circumstances related to the provision of person-centred paediatric diabetes care.
Building long-term relationships with adolescents, the establishment of a multidisciplinary care team and ensuring adequate documentation are vital for the delivery of person-centred care (PCC). Furthermore, a PCC process and/or practice requires more than the mere expression of person-centred values. The contribution of this study is that it highlights the necessity of facilitating and safeguarding the organization of PCC, for which three processes are central: 1. Facilitating long-term relationships with adolescents and their families; 2. Facilitating multi-professional teamwork; and 3. Ensuring adequate documentation.
Three processes emerged as important for the functioning of the multidisciplinary team when caring for adolescents with type 1 diabetes: building a long-term relationship, integrating knowledge by means of multidisciplinary team work and ensuring adequate documentation. This study demonstrates the importance of clearly defining and making use of the specific role of each team member in the paediatric diabetes care unit (PDCU). Team members should receive training in PCC and a PCC approach should form the foundation of all diabetes care. Every adolescent suffering from type 1 diabetes should be offered individual treatment and support according to her/his needs. However, more research is required to determine how a PCC approach can be integrated into adolescent diabetes care, and especially how PCC education programmes for team members should be implemented.
Extant research suggests that there is a strong social component to Evidence-Based Medicine (EBM) adoption due to the fact that professional networks amongst physicians are strongly associated with their attitudes towards EBM. Despite this evidence, it is still unknown whether individual attitudes to use scientific evidence in clinical decision-making influence the position that physicians hold in their professional network. This paper explores how physicians' attitudes towards EBM is related to the network position they occupy within healthcare organizations.
Data pertain to a sample of Italian physicians, whose professional network relationships, demographics and work-profile characteristics were collected. A social network analysis was performed to capture the structural importance of physicians in the collaboration network by the means of a core-periphery analysis and the computation of network centrality indicators. Then, regression analysis was used to test the association between the network position of individual clinicians and their attitudes towards EBM.
Findings documented that the overall network structure is made up of a dense cohesive core of physicians and of less connected clinicians who occupy the periphery. A negative association between the physicians' attitudes towards EBM and the coreness they exhibited in the professional network was also found. Network centrality indicators confirmed these results documenting a negative association between physicians' propensity to use EBM and their structural importance in the professional network.
Attitudes that physicians show towards EBM are related to the part (core or periphery) of the professional networks to which they belong as well as to their structural importance. By identifying virtuous attitudes and behaviors of professionals within their organizations, policymakers and executives may avoid marginalization and stimulate integration and continuity of care, both within and across the boundaries of healthcare providers.
The study goal was to assess indices of continuity of care in the primary care setting and their association with health outcomes and healthcare services utilization, given the reported importance of continuity regarding quality of care and healthcare utilization.
The study included a random sample of enrollees from Clalit Health Services 19 years-of-age or older who visited their primary care clinic at least three times in 2009. Indices of continuity of care were computed, including the Usual Provider Index (UPC), Modified Modified Continuity Index (MMCI), Continuity of Care Index (COC), and Sequential Continuity (SECON). Quality measures of preventive medicine and healthcare services utilization and their costs were assessed as outcomes.
1,713 randomly sampled patients were included in the study (mean age: 48.9 ± 19.2, 42% males). Continuity of care indices were: UPC: 0.75; MMCI: 0.81; COC: 0.67; SECON: 0.70. After controlling for patient characteristics in a multivariate analysis, a statistically significant association was found between higher values of UPC, COC, and SECON and a decrease in the number and cost of ED visits. Higher MMCI values were associated with a greater number and higher costs of medical consultation visits. Continuity of care indices were associated with BMI measurements, and inversely associated with blood pressure measurements. No association was found with other quality indicators, e.g., screening tests for cancer.
Several continuity of care indices were associated with decreased number and costs of ED visits. There were both positive and negative associations of continuity of care indices with different aspects of healthcare utilization. The relatively small effects of continuity might be due to the consistently high levels of continuity in Clalit Health Services.
Physicians are embedded in informal networks that result from their sharing of patients, information, and behaviors.
To identify professional networks among physicians, examine how such networks vary across geographic regions, and determine factors associated with physician connections.
Using methods adopted from social network analysis, Medicare administrative data from 2006 were used to study 4,586,044 Medicare beneficiaries seen by 68,288 physicians practicing in 51 hospital referral regions (HRRs). Distinct networks depicting connections between physicians (defined based on shared patients) were constructed for each of the 51 HRRs.
Variation in network characteristics across HRRs and factors associated with physicians being connected.
The number of physicians per HRR ranged from 135 in Minot, North Dakota, to 8197 in Boston, Massachusetts. There was substantial variation in network characteristics across HRRs. For example, the mean (SD) adjusted degree (number of other physicians each physician was connected to per 100 Medicare beneficiaries) across all HRRs was 27.3 (range, 11.7-54.4); also, primary care physician relative centrality (how central primary care physicians were in the network relative to other physicians) ranged from 0.19 to 1.06, suggesting that primary care physicians were more than 5 times more central in some markets than in others. Physicians with ties to each other were far more likely to be based at the same hospital (69.2% of unconnected physician pairs vs 96.0% of connected physician pairs; adjusted rate ratio, 0.12 [95% CI, 0.12-0.12]; P < .001), and were in closer geographic proximity (mean office distance of 21.1 km for those with connections vs 38.7 km for those without connections, P < .001). Connected physicians also had more similar patient panels in terms of the race or illness burden than unconnected physicians. For instance, connected physician pairs had an average difference of 8.8 points in the percentage of black patients in their 2 patient panels compared with a difference of 14.0 percentage points for unconnected physician pairs (P < .001).
Network characteristics vary across geographic areas. Physicians tend to share patients with other physicians with similar physician-level and patient-panel characteristics.
A new measure of centrality, CF, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman's original measure, CB, the new measure differs from the original in two important ways. First, CF is defined for both valued and non-valued graphs. This makes CF applicable to a wider variety of network datasets. Second, the computation of CF is not based on geodesic paths as is CB but on all the independent paths between all pairs of points in the network.
Ongoing care for chronic conditions such as diabetes is best provided by a range of health professionals working together. There are challenges in achieving this where collaboration crosses organisational and sector boundaries. The aim of this article is to explore the influence of power dynamics and trust on collaboration between health professionals involved in the management of diabetes and their impact on patient experiences.
A qualitative case study conducted in a rural city in Australia. Forty five health service providers from nineteen organisations (including fee-for-service practices and block funded public sector services) and eight patients from two services were purposively recruited. Data was collected through semi-structured interviews that were audio-taped and transcribed. A thematic analysis approach was used using a two-level coding scheme and cross-case comparisons.
Three themes emerged in relation to power dynamics between health professionals: their use of power to protect their autonomy, power dynamics between private and public sector providers, and reducing their dependency on other health professionals to maintain their power. Despite the intention of government policies to support more shared decision-making, there is little evidence that this is happening. The major trust themes related to role perceptions, demonstrated competence, and the importance of good communication for the development of trust over time. The interaction between trust and role perceptions went beyond understanding each other's roles and professional identity. The level of trust related to the acceptance of each other's roles. The delivery of primary and community-based health services that crosses organisational boundaries adds a layer of complexity to interprofessional relationships. The roles of and role boundaries between and within professional groups and services are changing. The uncertainty and vulnerability associated with these changes has affected the level of trust and mistrust.
Collaboration across organisational boundaries remains challenging. Power dynamics and trust affect the strategic choices made by each health professional about whether to collaborate, with whom, and to what level. These decisions directly influenced patient experiences. Unlike the difficulties in shifting the balance of power in interprofessional relationships, trust and respect can be fostered through a mix of interventions aimed at building personal relationships and establishing agreed rules that govern collaborative care and that are perceived as fair.
Physicians often rely on colleagues for new information and advice about the care of their patients.
Evaluate the network of influential discussions among primary care physicians in a hospital-based academic practice.
Survey of physicians about influential discussions with their colleagues regarding women's health issues. We used social network analysis to describe the network of discussions and examined factors predictive of a physician's location in the network.
All 38 primary care physicians in a hospital-based academic practice.
Location of physician within the influential discussion network and relationship with other physicians in the network.
Of 33 responding physicians (response rate = 87%), the 5 reporting expertise in women's health were more likely than others to be cited as sources of influential information (odds ratio [OR] 6.81, 95% Bayesian confidence interval [CI] 2.25-23.81). Physicians caring for more women were also more often cited (OR 1.03, 95% CI 1.01-1.05 for a 1 percentage-point increase in the proportion of women patients). Influential discussions were more frequent among physicians practicing in the same clinic within the practice than among those in different clinics (OR 5.03, 95% CI 3.10-8.33) and with physicians having more weekly clinical sessions (OR 1.33, 95% CI 1.15 to 1.54 for each additional session).
In the primary care practice studied, physicians obtained information from colleagues with greater expertise and experience as well as colleagues who were accessible based on location and schedule. It may be possible to organize practices to promote more rapid dissemination of high-quality evidence-based medicine.
The goal was to examine nursing team structure and its relationship with family satisfaction.
We used electronic health records to create patient-based, 1-mode networks of nursing handoffs. In these networks, nurses were represented as nodes and handoffs as edges. For each patient, we calculated network statistics including team size and diameter, network centrality index, proportion of newcomers to care teams according to day of hospitalization, and a novel measure of the average number of shifts between repeat caregivers, which was meant to quantify nursing continuity. We assessed parental satisfaction by using a standardized survey.
Team size increased with increasing length of stay. At 2 weeks of age, 50% of shifts were staffed by a newcomer nurse who had not previously cared for the index patient. The patterns of newcomers to teams did not differ according to birth weight. When the population was dichotomized according to median mean repeat caregiver interval value, increased reports of problems with nursing care were seen with less-consistent staffing by familiar nurses. This relationship persisted after controlling for factors including birth weight, length of stay, and team size.
Family perceptions of nursing care quality are more strongly associated with team structure and the sequence of nursing participation than with team size. Objective measures of health care team structure and function can be examined by applying network analytic techniques to information contained in electronic health records.
Background:
Several studies have discussed the benefits of multidisciplinary collaboration in primary care. However, what remains unclear is how collaboration is undertaken in a multidisciplinary manner in concrete terms.
Objective:
To identify how multidisciplinary teams in primary care collaborate, in regards to the professionals involved in the teams and the collaborative activities that take place, and determine whether these characteristics and practices are present across disciplines and whether collaboration affects clinical outcomes.
Methods:
A systematic literature review of past research, using the MEDLINE, ScienceDirect and Web of Science databases.
Results:
Four types of team composition were identified: specialized teams, highly multidisciplinary teams, doctor-nurse-pharmacist triad and physician-nurse centred teams. Four types of collaboration within teams were identified: co-located collaboration, non-hierarchical collaboration, collaboration through shared consultations and collaboration via referral and counter-referral. Two combinations were commonly repeated: non-hierarchical collaboration in highly multidisciplinary teams and co-located collaboration in specialist teams. Fifty-two per cent of articles reported positive results when comparing collaboration against the non-collaborative alternative, whereas 16% showed no difference and 32% did not present a comparison.
Conclusion:
Overall, collaboration was found to be positive or neutral in every study that compared collaboration with a non-collaborative alternative. A collaboration typology based on objective measures was devised, in contrast to typologies that involve interviews, perception-based questionnaires and other subjective instruments.
Around 10% of the population suffers from diabetes, and this percentage is expected to rise. Healthcare guidelines propose a multidisciplinary, collaborative approach for treatment. However, there is little data to understand whether healthcare professionals are actually collaborating and how this collaboration takes place. We analyzed 4 years of data from 3 healthcare centers in Chile, corresponding to 2,838 patients. Patients were classified according to the composition of the healthcare team into four categories: highly multidisciplinary teams, specialized teams, physician-nurse centered teams, and non-collaborative treatment. Our results show that team prevalence is related to patient and healthcare center characteristics.
Objective:
To improve Dutch maternity care, professionals start working in interdisciplinary patient-centred networks, which includes the patients as a member. The introduction of the case manager is expected to work positively on both the individual and the network level. However, case management is new in Dutch maternity care. The present study aims to define the profession that would be most suitable to fulfil the role of case manager.
Design:
The maternal care network in the Nijmegen region was determined by using Social Network Analysis (SNA). SNA is a quantitative methodology that measures and analyses patient-related connections between different professionals working in a network. To identify the case manager we focused on the position, reach, and connections in the network of the maternal care professionals.
Setting:
Maternity healthcare professionals in a single region of the Netherlands with an average of 4,500 births/year.
Participants:
The participants were 214 individual healthcare workers from eight different professions.
Measurements and findings:
The total network showed 3948 connections between 214 maternity healthcare professionals with a density of 0.08. Each profession had some central individuals in the network. The 52 community-based midwives were responsible for 51% of all measured connections. The youth health doctors and nurses were mostly situated on the periphery and less connected. The betweenness centrality had the highest score in obstetricians and community-based midwives. Only the community-based midwives had connections with all other groups of professions. Almost all professionals in the network could reach other professionals in two steps.
Objective
Demonstrate the feasibility of implementing a collaborative care program for poorly-controlled type 2 diabetes and complex behavioral health disorders in an urban academically-affiliated safety net primary care clinic.
Methods
This retrospective cohort study evaluates multidisciplinary team care approach to diabetes in a safety net clinic, and included 634 primary care clinic patients with hemoglobin A1c (HbA1c) > 9%. HbA1c, blood pressure, and depression severity were assessed at the initial visit and at the end of treatment, and compared to those of patients who were not referred to the team.
Results
The 151 patients referred to the program between March 2013 and November 2014 had a higher initial mean HbA1c: 10.6% vs. 9.4%, and were more likely to have depression (p = 0.006), anxiety (p = 0.04), and bipolar disorder (p = 0.03), compared to the 483 patients who were not referred. During the 18-month study period, there was a mean decrease in HbA1c of 0.9 (10.6 to 9.4) among those referred to the team, compared to a mean decrease of 0.2 (9.4 to 9.2) among those not referred. This was a significantly greater percent change in HbA1c (p = 0.008).
Conclusion
The integration of behavioral healthcare into chronic care management of patients with diabetes is a promising strategy to improve outcomes among the high risk population in safety net settings.
BACKGROUND
Obesity and diabetes family history are the two strongest risk factors for type 2 diabetes (T2D). Prior work shows that an individual’s obesity risk is associated with obesity in social contacts, but whether T2D risk follows similar patterns is unknown. OBJECTIVE
We aimed to estimate the relationship between obesity or diabetes in an individual’s social contacts and his/her T2D risk. We hypothesized that obesity and diabetes in social contacts would increase an individual’s T2D risk. DESIGNThis was a retrospective analysis of the community-based Framingham Offspring Study (FOS). PARTICIPANTSFOS participants with T2D status, height and weight, and at least one social contact were eligible for this study (n = 4797 at Exam 1). Participants’ interpersonal ties, cardiometabolic and demographic variables were available at eight exams from 1971 to 2008, and a T2D additive polygenic risk score was measured at the fifth exam. MAIN MEASURESPrimary exposures were T2D (fasting glucose ≥ 7 mmol/L or taking diabetes medications) and obesity status (BMI ≥ 30 kg/m2) of social contacts at a prior exam. Primary outcome was incident T2D in participants. KEY RESULTSIncident T2D was associated with having a social contact with diabetes (OR 1.32, p = 0.004) or with obesity (OR 1.21, p = 0.004). In stratified analyses, incident T2D was associated with diabetes in siblings (OR 1.64, p = 0.001) and obesity in spouses (OR 1.54, p = 0.0004). The associations between diabetes and obesity in social contacts and an individual’s incident diabetes risk were stronger in individuals with a high diabetes genetic risk score. CONCLUSIONST2D and obesity in social contacts, particularly siblings and spouses, were associated with an individual’s risk of incident diabetes even after accounting for parental T2D history. Assessing risk factors in an individual’s siblings and spouses can inform T2D risk; furthermore, social network based lifestyle interventions involving spouses and siblings might be a novel T2D prevention approach.
Objective:To determine whether the number and severity of diabetes complications are associated with increased risk of mortality and hospitalizations. Study Design: Validation sample. Methods:The Diabetes Complications Severity Index (DCSI) was developed from automated clinical baseline data of a primary care diabetes cohort and compared with a simple count of complications to predict mortality and hospitalizations. Cox proportional hazard and Poisson regression models were used to predict mortality and hospitalizations, respectively. Results: Of 4229 respondents, 356 deaths occurred during 4 years of follow-up. Those with 1 complication did not have an increased risk of mortality, whereas those with 2 complications (hazard ratio [HR) = 1.90, 95% confidence interval [CI] = 1.27,2.83), 3 complications (HR = 2.66, 95% CI = 1.77, 4.01), 4 complications (HR = 3.41, 95% CI = 2.18, 5.33), and >= 5 complications (HR = 7.18, 95% CI = 4.39, 11.74) had greater risk of death. Replacing the complications count with the DCSI showed a similar mortality risk. Each level of the continuous DCSI was associated with a 1.34-fold (95% CI = 1.28, 1.41) greater risk of death. Similar results were obtained for the association of the DCSI with risk of hospitalization. Comparison of receiver operating characteristic curves verified that the DCSI was a slightly better predictor of mortality than a count of complications (P<.0001). Conclusion: Compared with the complications count, the DCSI performed slightly better and appears to be a useful too] for prediction of mortality and risk of hospitalization.
Previous studies have documented the application of administrative claim dataset for health services research purposes. In addition to administrative and billing details of healthcare services, insurance claim datasets can reveal important information regarding professional interactions or links that evolve among healthcare service providers through, for example, informal knowledge sharing. The aim of this study is to develop a research framework, which uses details of such professional interactions, to learn about effective healthcare coordination and collaboration. The proposed framework has been exercised to analyse Patient-centric Care Coordination Network and Physician Collaboration Network. The usefulness of this framework and its applications in exploring different collaborative efforts of healthcare service providers have been discussed in this paper.
Although considerable progress has been made in understanding networks, their structure, and their development, little has been known about their effectiveness in the health care setting and their contributions to quality of care and patient safety.The purpose of this study was to examine studies using social network analysis (SNA) in the health care workforce and assess factors contributing to social network and their relationships with care processes and patient outcomes.
We identified all published peer-reviewed SNA articles in CINAHL, PubMed, PsycINFO, JSTOR, Medline (OVID), and Web of Science databases until April 2013.
Twenty-nine published articles met the inclusion criteria. Current evidence of the health care workforce's social networks reveals the nature of social ties are related to personal characteristics, practice setting, and types of patients. Few studies also revealed the social network effects adoption and the use of a health information system, patient outcomes, and coordination.
Current studies on the social ties of health care workforce professionals include several assessments of inefficiencies. The level of technical sophistication in studies tends to be low. Future study using enhanced sophistication in study design, analysis, and patient outcome testing are warranted to fully leverage the potential of SNA in health care studies.
Copyright © 2015 Elsevier Inc. All rights reserved.
Background
Nurses frequently work as part of both uni- and multidisciplinary teams. Communication between team members is critical in the delivery of quality care. Social network analysis is increasingly being used to explore such communication. AimTo explore the use of social network analysis involving nurses either as subjects of the study or as researchers. Methods
Standard systematic review procedures were applied to identify nurse-related studies that utilize social network analysis. A comparative thematic approach to synthesis was used. Both published and grey literature written in English, Spanish and Portuguese between January 1965 and December 2013 were identified via a structured search of CINAHL, SciELO and PubMed. In addition, Google and Yahoo search engines were used to identify additional grey literature using the same search strategy. ResultsForty-three primary studies were identified with literature from North America dominating the published work. So far it would appear that no author or group of authors have developed a programme of research in the nursing field using the social network analysis approach although several authors may be in the process of doing so. LimitationsThe dominance of literature from North America may be viewed as problematic as the underlying structures and themes may be an artefact of cultural communication norms from this region. Conclusions
The use of social network analysis in relation to nursing and by nurse researchers has increased rapidly over the past two decades. The lack of longitudinal studies and the absence of replication across multiple sites should be seen as an opportunity for further research. Implication for Nursing and Health PolicyThis analytical approach is relatively new in the field of nursing but does show considerable promise in offering insights into the way information flows between individuals, teams, institutions and other structures. An understanding of these structures provides a means of improving communication.
Aims
The primary aim of this study was to evaluate the impact of pharmaceutical care interventions on glycemic control and other health-related clinical outcomes in patients with type 2 diabetes patients in Jordan.
Methods
A randomized controlled clinical trial was conducted on 106 patients with uncontrolled type 2 diabetes seeking care in the diabetes clinics at Jordan University Hospital. Patients were randomly allocated into control and intervention group. The intervention group patients received pharmaceutical care interventions developed by the clinical pharmacist in collaboration with the physician while the control group patients received usual care without clinical pharmacist's input. Fasting blood glucose and HbA1c were measured at the baseline, at three months, and six months intervals for both intervention and control groups.
Results
After the six months follow-up, mean of HbA1c and FBS of the patients in the intervention group decreased significantly compared to the control group patients (P < 0.05). Also, the results indicated that mean scores of patients’ knowledge about medications, knowledge about diabetes and adherence to medications and diabetes self-care activities of the patients in the intervention group increased significantly compared to the control group (P < 0.05).
Conclusions
This study demonstrated an improvement in HbA1c, FBS, and lipid profile, in addition to self-reported medication adherence, diabetes knowledge, and diabetes self-care activities in patients with type 2 diabetes who received pharmaceutical care interventions. The results suggest the benefits of integrating clinical pharmacist services in multidisciplinary healthcare team and diabetes management in Jordan.
Complex pathologies associated with chronic health conditions must be dealt in a coordinated way and the ‘multidisciplinary team’ approach (MDTA) represents the most efficacious way of managing these patients. Over the last 25 years, the initial limited field for joint interventions by several specialists has been progressively expanded and this article reviews some of the conditions in which the MDTA has found useful application.
This has been the case in fields as diverse as primary healthcare, oncology, diabetes, cardiovascular, chronic kidney diseases and high-risk pregnancy. In the latter situation, an MDTA can offer clear advantages for pregnancies in solid organ recipient women. In these patients, a close collaboration is mandatory between a series of dedicated physicians (including, but not limited to, infertility and maternal–foetal medicine specialists, obstetricians, paediatricians, transplant physicians, geneticists and psychologists). Such a team should be active before, during and after pregnancy and should cope with all their reproductive health needs.
The social network model is powerful enough to provide for the analysis and study of a variety of application domains from daily life, including health care and health informatics. After the widespread appearance of automated tools capable of deriving and analyzing social networks, social network analysis (SNA) and mining in the health care domain has recently received considerable attention for its key role in understanding how various bodies within the health care system form communities and how they are socially connected with each other. This understanding helps enhance the organizational structures and process flows, among others. In this article, we show how SNA techniques can solve issues in the medical referral system in the Canadian health care system and the like, by analyzing the social network of general practitioners (GPs) and specialists (SPs). One of the main targets is to optimize the communication between GPs and SPs with hopes of decreasing the waiting time of patients to be seen by SPs. Various SNA and mining techniques are described and analyzed, backed by reporting some experimental results.
Previous studies have documented the effect of collaboration among physicians on the effectiveness in delivering health services and in producing better patient outcomes. However, there is no systematic empirical study suggesting the underlying relationship between the collaboration network of physicians and its effect on hospital outcomes (i.e., hospitalization cost and readmission rate). In this study, we first propose a way to capture collaboration network among physicians from their visiting information to patients. Then we explore the effect of different attributes (i.e., degree centrality, betweenness centrality, and network density) of physician collaboration network (PCN) on hospital outcomes. Our results show that degree centrality (i.e., level of involvement) and network density (i.e., level of connectedness) of PCN are negatively correlated with hospitalization cost and readmission rate. In contrast, betweenness centrality (i.e., capacity to control the flow of information) is found positively correlated with hospitalization cost and readmission rate. In their respective hospitals, healthcare managers or administrators may follow our research findings to reduce cost and improve quality (i.e., lower readmission rate).
Assessing care continuity is important in evaluating the impact of health care reform and changes to health care delivery. Multiple measures of care continuity have been developed for use with claims data.
This study examined whether alternative continuity measures provide distinct assessments of coordination within predefined episodes of care.
This was a retrospective cohort study using 2008-2009 claims files for a national 5% sample of beneficiaries with congestive heart failure, chronic obstructive pulmonary disease, and diabetes mellitus.
Correlations among 4 measures of care continuity-the Bice-Boxerman Continuity of Care Index, Herfindahl Index, usual provider of care, and Sequential Continuity of Care Index-were derived at the provider- and practice-levels.
Across the 3 conditions, results on 4 claims-based care coordination measures were highly correlated at the provider-level (Pearson correlation coefficient r=0.87-0.98) and practice-level (r=0.75-0.98). Correlation of the results was also high for the same measures between the provider- and practice-levels (r=0.65-0.92).
Claims-based care continuity measures are all highly correlated with one another within episodes of care.
Teaching clinics are an important source of care for urban, minority, underserved communities and face great challenges to improve quality of care for diabetics. This study examined the impact of continuity with the same primary care provider on health care process and outcome measures for patients with diabetes treated at an urban, family medicine resident teaching practice. The Modified Modified Continuity of Care Index was used to measure care continuity. The diabetes care quality measures were based on the NCQA HEDIS and Diabetes Recognition Program. Low levels of care continuity were associated with poor HbA1c control and higher levels of care continuity were associated with good LDL control. These findings suggest that improving care continuity should be considered in a systems-based approach to address disparities in diabetes care. Additional research is needed to include the patient's perspective in measuring care continuity and patient outcomes.
Effective provisioning of healthcare services during patient hospitalization requires collaboration involving a set of interdependent complex tasks, which needs to be carried out in a synergistic manner. Improved patients' outcome during and after hospitalization has been attributed to how effective different health services provisioning groups carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among physicians on the effective outcome in delivering health services for improved patient outcomes. However, there are very few systematic empirical studies with a focus on the effect of collaboration networks among healthcare professionals and patients' medical condition. On the basis of the fact that collaboration evolves among physicians when they visit a common hospitalized patient, in this study, we first propose an approach to map collaboration network among physicians from their visiting information to patients. We termed this network as physician collaboration network (PCN). Then, we use exponential random graph (ERG) models to explore the microlevel network structures of PCNs and their impact on hospitalization cost and hospital readmission rate. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks such as PCN. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the electronic health insurance claims dataset of a very large Australian health insurance organization, we construct and model PCNs. We notice that the 2-star (subset of 3 nodes in which 1 node is connected to each of the other 2 nodes) parameter of ERG has significant impact on hospitalization cost. Further, we identify that triangle (subset of 3 nodes in which each node is connected to the rest 2 nodes), alternative k-star (subset of k nodes in which 1 node is connected to each of other k - 1 nodes), and alternative k - 2 path (subset of k nodes in which, between a specific pair of nodes, there exists k - 2 paths of length 2) parameters of ERG have impact on the hospital readmission rate. Our findings can have implications for healthcare administrators or managers who could potentially improve the practice cultures in their organizations by following these outcomes. Copyright © 2013 John Wiley & Sons, Ltd.
Background:
Multidisciplinary care (MDC) has been proposed as a potential strategy to address the rising challenges of modern health issues. However, it remains unclear as to how patients' health connections may impact on multidisciplinary processes and outcomes.
Objectives:
This research aims to gain a deeper understanding of patients' potential role in MDC: i) describe patients' health networks, ii) compare different care groups, iii) gain an understanding of the nature and extent of their interactions, and iv) identify the role of pharmacists within patient networks.
Methods:
In-depth, semi-structured interviews were conducted with asthma patients from Sydney, Australia. Participants were recruited from a range of standard asthma health care access points (community group) and a specialized multidisciplinary asthma clinic (clinic group). Quantitative social network analysis provided structural insight into asthma networks while qualitative social network analysis assisted in interpretation of network data.
Results:
A total of 47 interviews were conducted (26 community group participants and 21 clinic group participants). Although participants' asthma networks consisted of a range of health care professionals (HCPs), these did not reflect or encourage MDC. Not only did participants favor minimal interaction with any HCP, they preferred sole-charge care and were found to strongly rely on lay individuals such as family and friends. While general practitioners and respiratory specialists were participants' principal choice of HCP, community pharmacists were less regarded.
Conclusion:
Limited opportunities were presented for HCPs to collaborate, particularly pharmacists. As patients' choices of HCPs may strongly influence collaborative processes and outcomes, this research highlights the need to consider patient perspectives in the development of MDC models in primary care.
Communication during patient handoffs has been widely implicated in patient safety issues. However, few studies have actually been able to quantify the relationship between handoffs and patient outcomes. We used *ORA, a dynamic network analysis tool, to examine handoffs between day and night shifts on seven units in three hospitals in the Southwest. Using *ORA's visualization and analysis capabilities, we examined the relationships between the handoff communication network metrics and a variety of patient safety quality and satisfaction outcomes. Unique network patterns were observed for different types of outcome variable (eg, safety, symptom management, self-care, and patient satisfaction). This exploratory project demonstrates the power of *ORA to identify communication patterns for large groups, such as patient care units. *ORA's network metrics can then be related to specific patient outcomes.
People are interconnected, and so their health is interconnected. In recognition of this social fact, there has been growing conceptual and empirical attention over the past decade to the impact of social networks on health. This article reviews prominent findings from this literature. After drawing a distinction between social network studies and social support studies, we explore current research on dyadic and supradyadic network influences on health, highlighting findings from both egocen-tric and sociocentric analyses. We then discuss the policy implications of this body of work, as well as future research directions. We conclude that the existence of social networks means that people's health is inter-dependent and that health and health care can transcend the individual in ways that patients, doctors, policy makers, and researchers should care about.
There is substantial variation in the cost and intensity of care delivered by US hospitals. We assessed how the structure of patient-sharing networks of physicians affiliated with hospitals might contribute to this variation.
We constructed hospital-based professional networks based on patient-sharing ties among 61,461 physicians affiliated with 528 hospitals in 51 hospital referral regions in the US using Medicare data on clinical encounters during 2006. We estimated linear regression models to assess the relationship between measures of hospital network structure and hospital measures of spending and care intensity in the last 2 years of life.
The typical physician in an average-sized urban hospital was connected to 187 other doctors for every 100 Medicare patients shared with other doctors. For the average-sized urban hospital an increase of 1 standard deviation (SD) in the median number of connections per physician was associated with a 17.8% increase in total spending, in addition to 17.4% more hospital days, and 23.8% more physician visits (all P<0.001). In addition, higher "centrality" of primary care providers within these hospital networks was associated with 14.7% fewer medical specialist visits (P<0.001) and lower spending on imaging and tests (-9.2% and -12.9% for 1 SD increase in centrality, P<0.001).
Hospital-based physician network structure has a significant relationship with an institution's care patterns for their patients. Hospitals with doctors who have higher numbers of connections have higher costs and more intensive care, and hospitals with primary care-centered networks have lower costs and care intensity.
Collaboration means working together to achieve a common goal or to solve a problem, and in modern businesses, it is an important factor for information sharing and quality. This is due to the ability of collaborations to shape the structure and behaviour of organisations through the pooling of expertise and standardising of work patterns.Grounded on complex network theory and collaborative design research, a mathematical model of information flow for analysing collaboration in organisations is proposed in this article. The model defines concepts for characterising organisational structures for collaboration and proposes indicators for assessing organisational behaviour in terms of collaboration within organisations. The article concludes by discussing the applications and limitations of the proposed model.
To examine the effects of continuity of care on healthcare utilization and expenses for patients with diabetes mellitus.
Longitudinal study based on claims data.
Data on healthcare utilization and expenses from a 7-year period (2000-2006) were gathered from claims data of the Taiwanese universal health insurance system. The continuity of care index (COCI) was analyzed, and the values were classified into 3 levels. Outcome variables included the likelihood of hospitalization and emergency department visit, pharmaceutical expenses for diabetes-related conditions, and total healthcare expenses for diabetes-related conditions. A generalized estimating equation that considered the effects of repeated measures for the same patients was applied to examine the effects of continuity of care on healthcare utilization and expenses.
Compared with patients who had low COCI scores, patients with high or medium COCI scores were less likely to be hospitalized for diabetes related conditions (odds ratio [OR] 0.26, 95% confidence interval [CI] 0.25, 0.27, and OR 0.58, 95% CI 0.56, 0.59, respectively) or to have diabetes-related emergency department visits (OR 0.34, 95% CI 0.33, 0.36, and OR 0.64, 95% CI 0.62, 0.66, respectively). Patients with low COCI scores incurred 737) in total healthcare expenses for diabetes-related conditions than patients with low COCI scores.
Better continuity of care was associated with less healthcare utilization and lower healthcare expenses for diabetic patients. Improving continuity of care might benefit diabetic patients.
We used ORA, a dynamic network analysis tool, to identify patient care unit communication patterns associated with patient safety and quality outcomes. Although ORA had previously had limited use in healthcare, we felt it could effectively model communication on patient care units.
Using a survey methodology, we collected communication network data from nursing staff on seven patient care units on two different days. Patient outcome data were collected via a separate survey. Results of the staff survey were used to represent the communication networks for each unit in ORA. We then used ORA's analysis capability to generate communication metrics for each unit. ORA's visualization capability was used to better understand the metrics.
We identified communication patterns that correlated with two safety (falls and medication errors) and three quality (e.g., symptom management, complex self care, and patient satisfaction) outcome measures. Communication patterns differed substantially by shift.
The results demonstrate the utility of ORA for healthcare research and the relationship of nursing unit communication patterns to patient safety and quality outcomes.
To assess whether connections between physicians based on shared patients in administrative data correspond with professional relationships between physicians.
Survey of physicians affiliated with a large academic and community physicians' organization and 2006 Medicare data from a 100 percent sample of patients in the Boston Hospital referral region.
We administered a web-based survey to 616 physicians (response rate: 63 percent) about referral and advice relationships with physician colleagues. Relationships measured by this questionnaire were compared with relationships assessed by patient sharing, measured using 2006 Medicare data. Each physician was presented with an individualized roster of physicians' names with whom they did and did not share patients based on the Medicare data.
The probability of two physicians having a recognized professional relationship increased with the number of Medicare patients shared, with up to 82 percent of relationships recognized with nine shared patients, overall representing a diagnostic test with an area under the receiver-operating characteristic curve of 0.73 (95 percent CI: 0.70-0.75). Primary care physicians were more likely to recognize relationships than medical or surgical specialists (p<.001).
Patient sharing identified using administrative data is an informative "diagnostic test" for predicting the existence of relationships between physicians. This finding validates a method that can be used for future research to map networks of physicians.