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Abstract Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system that does not provide universal coverage, albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides coverage for the majori...
Citations
... The elbow method is a commonly used technique to determine the optimal number of clusters for K-mode clustering (Batarseh et al., 2020). The method involves calculating the within-cluster sum of squares (WCSS) for a range of cluster numbers and plotting the results against the number of clusters. ...
Precision healthcare is an emerging field of science that utilizes an individual’s health information, context, and genetics to provide more personalized diagnostics and treatments. In this manuscript, we leverage that concept and present a group of machine learning models for precision gaming. These predictive models guide adolescents through best practices related to their health. The use case deployed is for girls in India through a mobile application released in three different Indian states. To evaluate the usability of the models, experiments are designed and data (demographic, behavioral, and health-related) are collected. The experimental results are presented and discussed.
... 2021 supplementary materials)). Indeed, data science studies concerned with 'Big Data' applications have cautioned about recognizing the limits of data 'repair' techniques and when additional primary data are required to avoid a 'garbage in, garbage out' situation (Baesens, 2014;Batarseh et al., 2020). While broad-scale database studies of the fossil record have been instrumental in building our understanding of major macroevolutionary patterns, biodiversity changes, and perhaps most significantly the 'Big 5' mass extinctions of the Phanerozoic Foote and Sepkoski, 1999;Alroy et al., 2008;Foote, 2016;Crampton et al., 2018), the quality of the data available, its applicability to particular questions, and the issues of sampling biases, resolution, and taphonomic effects must be carefully weighed when addressing hypotheses relating to finer-scale patterns and processes. ...
... Conclusion: Our findings indicate that the therapeutic concordance approach significantly reduces ADRs in TRH patients. KEYWORDS adherence, adverse drug reactions, blood pressure, compliance, concordance, hypertension, pharmacologic resistance, resistant hypertension Introduction While health care systems are known to be highly incentivized to provide care for acute illnesses such as myocardial infarction and stroke, they are not well designed for preventive care (1)(2)(3). Primary prevention of cardiovascular disease may potentially be better managed in a system non-centered on the acute care model (4)(5)(6). ...
Background
Adverse drug reactions (ADRs) remain among the leading causes of therapy-resistant hypertension (TRH) and uncontrolled blood pressure (BP). We have recently reported beneficial results in BP control in patients with TRH adopting an innovative approach, defined as therapeutic concordance, in which trained physicians and pharmacists reach a concordance with patients to make them more involved in the therapeutic decision-making process.Methods
The main scope of this study was to investigate whether the therapeutic concordance approach could lead to a reduction in ADR occurrence in TRH patients. The study was performed in a large population of hypertensive subjects of the Campania Salute Network in Italy (ClinicalTrials.gov Identifier: NCT02211365).ResultsWe enrolled 4,943 patients who were firstly followed-up for 77.64 ± 34.44 months, allowing us to identify 564 subjects with TRH. Then, 282 of these patients agreed to participate in an investigation to test the impact of the therapeutic concordance approach on ADRs. At the end of this investigation, which had a follow-up of 91.91 ± 54.7 months, 213 patients (75.5%) remained uncontrolled while 69 patients (24.5%, p < 0.0001) reached an optimal BP control. Strikingly, during the first follow-up, patients had complained of a total of 194 ADRs, with an occurrence rate of 68.1% and the therapeutic concordance approach significantly reduced ADRs to 72 (25.5%).Conclusion
Our findings indicate that the therapeutic concordance approach significantly reduces ADRs in TRH patients.
... Predictive analytics, propelled by AI algorithms, dissect diverse datasets, including patient health records, environmental factors, and genetic information, to identify patterns indicative of potential health risks. Leveraging machine learning models, healthcare providers can proactively intervene, offering personalized preventive measures to individuals at risk and mitigating the onset of diseases before clinical manifestation [33][34][35][36][37][38]. ...
The progress of Artificial Intelligence (AI) has transformed various sectors, and its incorporation into healthcare carries immense potential for the future of medical practices. This paper investigates the profound impact of AI on autonomous healthcare, specifically concentrating on personalized medicine and disease prognosis. The goal is to leverage AI's capabilities to augment diagnostic precision, treatment efficacy, and overall healthcare results. In the pursuit of personalized medicine, AI is applied to scrutinize extensive datasets, encompassing genomics, proteomics, and patient records. Computational genomics employs AI algorithms to unravel intricate genetic information, facilitating the discovery of tailored treatment approaches based on an individual's distinct genetic composition. Additionally, AI-driven proteomic analysis contributes to comprehending protein interactions and discovering biomarkers, paving the way for targeted therapies customized to each patient's specific requirements. The integration of AI in medical imaging is a pivotal element of personalized medicine. Machine learning algorithms are deployed to interpret radiological images, enabling the early detection and characterization of diseases. Subfields such as radiomics and pathology informatics utilize AI to extract quantitative data from medical images, providing a more holistic understanding of disease patterns and progression. Disease prediction constitutes another focal point of this research, wherein AI plays a pivotal role in analyzing diverse healthcare data to recognize early indications of diseases. Predictive modeling, fueled by machine learning algorithms, empowers proactive health condition management, potentially averting the onset of diseases through timely interventions. Subfields like predictive analytics and clinical decision support systems contribute to the creation of robust models aiding healthcare professionals in making well-informed decisions. This paper also explores the ethical considerations and challenges tied to the integration of AI in healthcare, underscoring the significance of responsible and transparent utilization of these technologies. As AI continues to advance, the convergence of technology and healthcare envisions a future where medical practices are not only more accurate and efficient but also tailored to meet the unique needs of each patient.
Keywords: Artificial Intelligence, Personalized medicine, Disease prediction, Autonomous healthcare, Human, Precision medicine.
... When creating new digital solutions, it is important to ensure that they are clinically meaningful, are impactful, are inclusive of diverse groups, and respond to the needs of these groups accordingly [6,10,32,33]. This evidence is also required for regulatory approval, which is a key aim of the Mobilise-D consortium [34][35][36]. ...
UNSTRUCTURED
Mobility is an important indicator of physical health. As such there is potential clinical value in being able to measure mobility accurately in a person’s home and daily life environment to help researchers and clinicians to better track changes and patterns in a person’s daily lives and activities. To do this, there is a need to create new ways of measuring walking. Recent advancements in digital technology are helping researchers to do this. However, before any new measure can be used, researchers, healthcare professionals and regulators need to know that the digital method is accurate and that it is both accepted and produces meaningful outcomes for the patients and clinicians. Researchers must therefore include patients, or members of patient organisations, in the development of such new tools in a process known as patient and public involvement and engagement (PPIE). Although the value and importance of PPIE activities is well-known, little guidance exists on how to do this in a meaningful way. This is particularly true within large research consortia that target multiple objectives, include multiple patient groups and work across many countries. Without clear guidance, the risk is that PPIE does not capture patient opinions and needs correctly, thereby reducing the usefulness and effectiveness of new tools. Mobilise-D is an example of such a large research consortium, that is looking to develop new digital outcome measures for real-world walking in patients with Parkinson’s Disease, Multiple Sclerosis, Chronic Obstructive Pulmonary Disease, and Proximal Femoral Fracture. This paper outlines how PPIE structures were developed in this consortium, providing detail about how this happened, the steps taken to implement PPIE, the experiences PPIE contributors have had within this process, the lessons learned from it, and recommendations for others who may want to do similar work in the future. The work outlined within this paper has provided the Mobilise-D consortium with a foundation from which future PPIE tasks can be created and managed with clearly defined collaboration between researchers and patient representatives across Europe. This paper provides guidance around the work required to set up PPIE structures within a large consortium, to promote and support the creation of meaningful and efficient PPIE related to the development of digital mobility outcomes.
... When creating new digital solutions, it is important to ensure that they are clinically meaningful, are impactful, are inclusive of diverse groups, and respond to the needs of these groups accordingly [6,10,32,33]. This evidence is also required for regulatory approval, which is a key aim of the Mobilise-D consortium [34][35][36]. ...
Although the value of patient and public involvement and engagement (PPIE) activities in the development of new interventions and tools is well known, little guidance exists on how to perform these activities in a meaningful way. This is particularly true within large research consortia that target multiple objectives, include multiple patient groups, and work across many countries. Without clear guidance, there is a risk that PPIE may not capture patient opinions and needs correctly, thereby reducing the usefulness and effectiveness of new tools. Mobilise-D is an example of a large research consortium that aims to develop new digital outcome measures for real-world walking in 4 patient cohorts. Mobility is an important indicator of physical health. As such, there is potential clinical value in being able to accurately measure a person’s mobility in their daily life environment to help researchers and clinicians better track changes and patterns in a person’s daily life and activities. To achieve this, there is a need to create new ways of measuring walking. Recent advancements in digital technology help researchers meet this need. However, before any new measure can be used, researchers, health care professionals, and regulators need to know that the digital method is accurate and both accepted by and produces meaningful outcomes for patients and clinicians. Therefore, this paper outlines how PPIE structures were developed in the Mobilise-D consortium, providing details about the steps taken to implement PPIE, the experiences PPIE contributors had within this process, the lessons learned from the experiences, and recommendations for others who may want to do similar work in the future. The work outlined in this paper provided the Mobilise-D consortium with a foundation from which future PPIE tasks can be created and managed with clearly defined collaboration between researchers and patient representatives across Europe. This paper provides guidance on the work required to set up PPIE structures within a large consortium to promote and support the creation of meaningful and efficient PPIE related to the development of digital mobility outcomes.
... Besides, thanks to big data availability and relevant developments concerning methods of analytics (artificial intelligence and machine learning algorithms and applications), it is legitimately achievable to design and implement proactive and preventive healthcare strategies and practices. Nevertheless, such endeavors might be negatively affected by legal aspects and privacy issues, as these cases might redact the availability of accessible and usable data [11]. Additionally, clustering is one of the benefits of data-driven healthcare practices to segment different groups to meet healthcare needs. ...
Data-driven health care is truly valuable and promising. As long as relevant data are gathered, probed, used, and managed in a good fashion, significant improvements in the dependability of healthcare practices are achievable. Nevertheless, unless privacy facets of relevant sensitive data are addressed, there are notable concerns regarding data-driven healthcare policies and applications. In general, technical and engineering facets of such interventions are concentered on to a greater extent, but privacy facets are not adequately addressed. This chapter highlights and discusses privacy issues in data-driven health care. A comprehensive review and distillation of pertinent literature and works yielded relevant results and interpretations. Purposefully, generic privacy issues are elaborated in the beginning. Additionally, areas for improvement regarding privacy issues in data-driven health care are underlined and discussed. People, policy, and technology aspects are also explained and deliberated. Moreover, how privacy is related to people and policy to ensure the success in data-driven healthcare practices is discussed in this chapter. Besides, people’s perceptions about privacy are distilled and reported. The focal impact of this chapter is to deliver a contemporary interpretation and discussion regarding privacy issues in data-driven health care. Product developers and managers, policy-makers, and pertinent researchers might benefit from this chapter in order to improve related knowledge and implementations.KeywordsPrivacyPolicyPeopleTechnologyHealth careBig dataBlockchain
... The old model of the health care industry waiting for a disease to manifest and then treat it has gradually been displaced by a model that uses signals to prevent the disease. This shift is due to acceleration of health information both in terms of the quantity and from various sources such as EHR, genomic data, wearable devices, and environmental sensors among others (Batarseh et al., 2020). Such apparent influence can be realized by using big data analytics by AI and ML specialists to identify individuals who are most likely to develop certain diseases and thus can be attended to quickly to prevent the development or aggravation of many diseases (Subbhuraam & Olatinwo, 2021). ...
... Despite these issues, the integration of multiple data points exposed an increased precision allowing care givers to counteract risks at really early stages than before. Batarseh et al. (2020) also showed that these early intervention models also reduced the incidence of full-blown type 2 diabetes among at-risk populations when measures of intervention were based on the prediction models presented in the study. ...
... Causal evidence indicated that big data analytics can be useful in predicting populations most susceptible to diabetes based on multiple variables analysed in big data. The study showed that when demographic data, socioeconomic characteristics, and environmental data are entered into machine learning algorithms, these algorithms perform population attribution and provide data on people or communities who are at greater risk of developing diabetes, (Batarseh et al., 2020). These outcomes provided healthcare providers with information that allowed for the implementation of improved targeted, as well as efficient, interventional approaches. ...
Diabetes is a lifelong condition which is associated with abnormally high blood sugar levels due to insufficient production of insulin or failure of the body to use the hormone efficiently. Due to a rising number of diabetes cases globally, the need to develop efficient and targeted approaches for the disease is more pressing than pre/application. Artificial intelligence (AI) and machine learning (ML) are among the most significant innovations in the healthcare industry, creating new positive directions for creating individualized plans for diabetes mellitus management. AI and ML are not just limited to handling medical and patients’ data, but it helps in robot assisted surgeries, virtual nursing professionals, and computer aided diagnosis. These technologies are not deploying human practitioners out of service, but instead heightening their capabilities culminating in a health win and decrease in cost. PROAC supports further research and development of these fast-growing technologies in healthcare, as we find ourselves on the brink of revolution that might change disease management and customization of treatment for many more years to come. This review has therefore involved using different electronic databases, including PubMed, Scopus and Google Scholar, to search for relevant published literature. The application of AI and ML has proved promising, and the research concluded that it can help develop a risk assessment model for early diagnosis and prevention of diabetes. Such algorithms look at a combination of multiple risk factors including family history, genetics, nutrition, biochemical markers, and physical activity level, which puts those with higher risk on special diet, exercise regimens or screening. Further, AI endorsing, decision support systems may provide tailored recommendations regarding treatment schedules based on patients’ characteristics such as insulin response, diet and exercise. Additionally, the continuous glucose monitoring (CGM) devices integrable with the machine learning algorithms can inform real-time information about glucose behaviors to make relevant alterations to doses of insulin and a dietary intake. This paper presents various benefits of adopting AI and ML in diabetes care: heterogeneous and accurate risk prediction, individualized therapies, and observation. But the problem of data quality, data privacy issues and the issue of interpretability and explainability of the trained AI models remains a problem in the use of AI in automated scheduling. The approaches require interdisciplinary collaborations across the medical field, data analytics, and policy departments in order to be properly applied and used.
... 11 The Institute of Medicine has estimated the missed prevention opportunities cost the US$55 Billion and Batarseh et al in their work have shown how big data and machine learning can be used for screening, early diagnosis and management of chronic diseases. 12 The potential of big data, AI and healthcare analytics are increasingly being demonstrated during COVID-19 pandemic from different countries. Predictive models with data inputs from different devices backed by IoT (internet of things) have helped in improving patient outcomes during COVID-19. ...
Background
Indian healthcare is rapidly growing and needs efficiency more than ever, which can be achieved by leveraging healthcare analytics. National Digital Health Mission has set the stage for digital health and getting the right direction from the very beginning is important. The current study was, therefore, undertaken to find what it takes for an apex tertiary care teaching hospital to leverage healthcare analytics.
Aim
To study the existing Hospital Information System (HIS) at AIIMS, New Delhi and assess the preparedness to leverage healthcare analytics.
Methodology
A three-pronged approach was used. First, concurrent review and detailed mapping of all running applications was done based on nine parameters by a multidisciplinary team of experts. Second, capability of the current HIS to measure specific management related KPIs was evaluated. Third, user perspective was obtained from 750 participants from all cadres of healthcare workers, using a validated questionnaire based on Delone and McLean model.
Results
Interoperability issues between applications running within the same institute, impaired informational continuity with limited device interface and automation were found on concurrent review. HIS was capturing data to measure only 9 out of 33 management KPIs. User perspective on information quality was very poor which was found to be due to poor system quality of HIS, though some functions were reportedly well supported by the HIS.
Conclusion
It is important for hospitals to first evaluate and strengthen their data generation systems/HIS. The three-pronged approach used in this study provides a template for other hospitals.
... Healthcare prevention, ranging from regular dental cleaning to collective initiatives to promote a healthier lifestyle, is one of the most important pillars of public health (1). Major gains in health can be accomplished through prevention (2). ...
Background: The benefits of prevention are widely recognized; ranging from avoiding disease onset to substantially reducing disease burden, which is especially relevant considering the increasing prevalence of chronic diseases. However, its delivery has encountered numerous obstacles in healthcare. While healthcare professionals play an important role in stimulating prevention, their behaviors can be influenced by incentives related to reimbursement schemes.
Purpose: The purpose of this research is to obtain a detailed description and explanation of how reimbursement schemes specifically impact primary, secondary, tertiary, and quaternary prevention.
Methods: Our study takes a mixed-methods approach. Based on a rapid review of the literature, we include and assess 27 studies. Moreover, we conducted semi-structured interviews with eight Dutch healthcare professionals and two representatives of insurance companies, to obtain a deeper understanding of healthcare professionals' behaviors in response to incentives.
Results: Nor fee-for-service (FFS) nor salary can be unambiguously linked to higher or lower provision of preventive services. However, results suggest that FFS's widely reported incentive to increase production might work in favor of preventive services such as immunizations but provide less incentives for chronic disease management. Salary's incentive toward prevention will be (partially) determined by provider-organization's characteristics and reimbursement. Pay-for-performance (P4P) is not always necessarily translated into better health outcomes, effective prevention, or adequate chronic disease management. P4P is considered disruptive by professionals and our results expose how it can lead professionals to resort to (over)medicalization in order to achieve targets. Relatively new forms of reimbursement such as population-based payment may incentivize professionals to adapt the delivery of care to facilitate the delivery of some forms of prevention.
Conclusion: There is not one reimbursement scheme that will stimulate all levels of prevention. Certain types of reimbursement work well for certain types of preventive care services. A volume incentive could be beneficial for prevention activities that are easy to specify. Population-based capitation can help promote preventive activities that require efforts that are not incentivized under other reimbursements, for instance activities that are not easily specified, such as providing education on lifestyle factors related to a patient's (chronic) disease.
