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What we can learn from Amazon for CDSS
Abstract
Health care continue to lag behind other industries, such as retail and financial services, in the use of decision-support-like tools. Amazon is particularly prolific in the use of advanced predictive and prescriptive analytics to assist its customers to purchase more, while increasing satisfaction, retention, repeat-purchases and loyalty. How can we do the same in health care? In this paper, we explore various elements of the Amazon website and Amazon's data science and big data practices to gather inspiration for redesigning clinical decision support in the health care sector. For each Amazon element we identified, we present one or more clinical applications to help us better understand where Amazon's
... More privacy-preservation and protection features (see fig. 1) is highly likely to be considered for other data that healthcare, biomedical and social sciences research requires; phenotypic and other highly-sensitive data is likely to fit Levels [2][3] (see fig. 2). Federation trends diverge in approaches, adopting emerging technologies and security standards. ...
... The latter can resource data kept outside the server, in the same organisation. DataSHIELD brings the potential to fit every of fig. 2. Existing organisational security features, implementation of privacy-preserving computations, and also the settings of privacy-preserving parametrers should lower disclosure increasing to Levels [2][3] (see fig. 2). Data governance tools and their application should maintain resilience to data protection and data preservation. ...
This unpublished paper needs some feedback from the community. it focuses on reviewing solutions to today's challenges
... Bioinformatics and population-health medical research has also generated large quantities of data stored in various digital forms [18]. A growing concern is becoming more apparent this data is awaiting to be stored and analysed by the immense analytical power offered by Internet-Based services and become exploitable business assets to improve business activities, and products development, and medical research [9,10,[19][20][21][22][23]. For example, the application of machine learning algorithms extends advanced statistical methodologies to analyse unstructured data (i.e., images, documents, social media entries). ...
An increasing demand and dependence of analyzing a data has been driven by “Big Data” and “Internet of Things (IoT)”. Scientific reproducibility, robustness and the cost of capturing new data has been improved through findable, accessible, interoperable, and reusable data sharing. Ethical and legal restrictions impose the use of privacy preservation and protection measures for any disclosive and sensitive information. We, therefore, present a possible model to support multidisciplinary research team to protect against disclosure of individual-level data and large datasets used in other disciplines. We argue a technology reliance is not enough and a continuous collaboration that adapt to new cyber-security, and data inferential threat is needed. We consequently conclude some standards could lead to closer collaboration to support research and innovation in the long term
... In 2011, the Canadian healthcare system spent $2.6 billion on preventable medicationrelated hospitalizations [1]. More than 7% of Canadians and almost 1 in 3 seniors take five or more prescribed medications, a situation commonly referred to as polypharmacy [2,3].Half of patients taking multiple medications do not take their medications as prescribed, and between40% to80% of the information communicated verbally by healthcare professionals is forgotten by the patient [4,5].This situation leads to preventable adverse drug events and directly impacts patient safety. ...
The objective of this study is to understand how the reason for use (RFU) or the indication for medications are used, its effects on the decision-making process, the implications, and the willingness among prescribers, pharmacists, and patients to share RFU information. Methods, semi-structured interviews were conducted to retrieve the information needed from a total of 60 participants. Results, pharmacists, prescribers, and patients generally have positive opinions about including RFU information in their communications. Conclusion, there is a general agreement among participants that sharing RFU information will improve patient safety.
... More than 7% of Canadians and almost 1 in 3 seniors take five or more prescribed medications, a situation commonly referred to as polypharmacy [2,3]. Half of patients taking multiple medications do not take their medications as prescribed, and between 40% to 80% of the information communicated verbally [4] by healthcare professionals is forgotten by the patient [5]. This situation leads to preventable adverse drug events and directly impacts patient safety. ...
The objective of this study is to understand how the reason for use (RFU) or the indication for medications are used, its effects on the decision-making process, the implications, and the willingness among prescribers, pharmacists, and patients to share RFU information. Methods, semi-structured interviews were conducted to retrieve the information needed from a total of 60 participants. Results, pharmacists, prescribers, and patients generally have positive opinions about including RFU information in their communications. Conclusion, there is a general agreement among participants that sharing RFU information will improve patient safety.
Background
A computerised clinical decision support system (CCDSS) is a technology that uses patient-specific data to provide relevant medical knowledge at the point of care. It is considered to be an important quality improvement intervention, and the implementation of CCDSS is growing substantially. However, the significant investments do not consistently result in value for money due to content, context, system and implementation issues. The Guideline Implementation with Decision Support (GUIDES) project aims to improve the impact of CCDSS through optimised implementation based on high-quality evidence-based recommendations. To achieve this, we will develop tools that address the factors that determine successful CCDSS implementation.
Methods/design
We will develop the GUIDES tools in four steps, using the methods and results of the Tailored Implementation for Chronic Diseases (TICD) project as a starting point: (1) a review of research evidence and frameworks on the determinants of implementing recommendations using CCDSS; (2) a synthesis of a comprehensive framework for the identified determinants; (3) the development of tools for use of the framework and (4) pilot testing the utility of the tools through the development of a tailored CCDSS intervention in Norway, Belgium and Finland. We selected the conservative management of knee osteoarthritis as a prototype condition for the pilot. During the process, the authors will collaborate with an international expert group to provide input and feedback on the tools.
Discussion
This project will provide guidance and tools on methods of identifying implementation determinants and selecting strategies to implement evidence-based recommendations through CCDSS. We will make the GUIDES tools available to CCDSS developers, implementers, researchers, funders, clinicians, managers, educators, and policymakers internationally. The tools and recommendations will be generic, which makes them scalable to a large spectrum of conditions. Ultimately, the better implementation of CCDSS may lead to better-informed decisions and improved care and patient outcomes for a wide range of conditions.
Protocol registration
PROSPERO, CRD42016033738
Electronic supplementary material
The online version of this article (doi:10.1186/s13012-016-0393-7) contains supplementary material, which is available to authorized users.
Adoption and use of Electronic Medical Records (EMRs) is continuing to rise across Canada, leading to more data being generated. These data, however, are not being captured in a standardized manner, they are not available for research, surveillance or health system management, and they are not having a real-time impact on healthcare providers at the point of care. Multiple stakeholders, including researchers and system evaluators, require easy access to high quality, structured data. As current EMRs are not able to effectively meet their needs, we engaged multiple stakeholders to assist in designing a solution. A total of 90 stakeholders from various backgrounds participated in an iterative joint design process. After incorporating the feedback of all stakeholders, we developed the design for a scalable platform for capturing structured, evidence-based data from all EMRs in Canada for research, health system management, clinical decision support and other purposes. We discuss the design specification for our proposed solution and explain how, using clinical forms, we can not only capture structured, high quality data from multiple EMRs, but also provide real-time guideline advice to providers at the point of care. The scalability of this proposed solution across multiple diseases and multiple EMRs is also explained. We further discuss the benefits and limitations of this proposed solution to several key stakeholder groups and address issues of privacy and security.
We systematically reviewed randomized controlled trials (RCTs) assessing the effectiveness of computerized decision support systems (CDSSs) featuring rule- or algorithm-based software integrated with electronic health records (EHRs) and evidence-based knowledge. We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Abstracts of Reviews of Effects. Information on system design, capabilities, acquisition, implementation context, and effects on mortality, morbidity, and economic outcomes were extracted.
Twenty-eight RCTs were included. CDSS use did not affect mortality (16 trials, 37395 patients; 2282 deaths; risk ratio [RR] = 0.96; 95% confidence interval [CI] = 0.85, 1.08; I ² = 41%). A statistically significant effect was evident in the prevention of morbidity, any disease (9 RCTs; 13868 patients; RR = 0.82; 95% CI = 0.68, 0.99; I ² = 64%), but selective outcome reporting or publication bias cannot be excluded. We observed differences for costs and health service utilization, although these were often small in magnitude.
Across clinical settings, new generation CDSSs integrated with EHRs do not affect mortality and might moderately improve morbidity outcomes.
To identify factors that differentiate between effective and ineffective computerised clinical decision support systems in terms of improvements in the process of care or in patient outcomes.
Meta-regression analysis of randomised controlled trials.
A database of features and effects of these support systems derived from 162 randomised controlled trials identified in a recent systematic review. Trialists were contacted to confirm the accuracy of data and to help prioritise features for testing.
"Effective" systems were defined as those systems that improved primary (or 50% of secondary) reported outcomes of process of care or patient health. Simple and multiple logistic regression models were used to test characteristics for association with system effectiveness with several sensitivity analyses.
Systems that presented advice in electronic charting or order entry system interfaces were less likely to be effective (odds ratio 0.37, 95% confidence interval 0.17 to 0.80). Systems more likely to succeed provided advice for patients in addition to practitioners (2.77, 1.07 to 7.17), required practitioners to supply a reason for over-riding advice (11.23, 1.98 to 63.72), or were evaluated by their developers (4.35, 1.66 to 11.44). These findings were robust across different statistical methods, in internal validation, and after adjustment for other potentially important factors.
We identified several factors that could partially explain why some systems succeed and others fail. Presenting decision support within electronic charting or order entry systems are associated with failure compared with other ways of delivering advice. Odds of success were greater for systems that required practitioners to provide reasons when over-riding advice than for systems that did not. Odds of success were also better for systems that provided advice concurrently to patients and practitioners. Finally, most systems were evaluated by their own developers and such evaluations were more likely to show benefit than those conducted by a third party.
Computerized clinical decision support systems (CCDSSs) for drug therapy management are designed to promote safe and effective medication use. Evidence documenting the effectiveness of CCDSSs for improving drug therapy is necessary for informed adoption decisions. The objective of this review was to systematically review randomized controlled trials assessing the effects of CCDSSs for drug therapy management on process of care and patient outcomes. We also sought to identify system and study characteristics that predicted benefit.
We conducted a decision-maker-researcher partnership systematic review. We updated our earlier reviews (1998, 2005) by searching MEDLINE, EMBASE, EBM Reviews, Inspec, and other databases, and consulting reference lists through January 2010. Authors of 82% of included studies confirmed or supplemented extracted data. We included only randomized controlled trials that evaluated the effect on process of care or patient outcomes of a CCDSS for drug therapy management compared to care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive.
Sixty-five studies met our inclusion criteria, including 41 new studies since our previous review. Methodological quality was generally high and unchanged with time. CCDSSs improved process of care performance in 37 of the 59 studies assessing this type of outcome (64%, 57% of all studies). Twenty-nine trials assessed patient outcomes, of which six trials (21%, 9% of all trials) reported improvements.
CCDSSs inconsistently improved process of care measures and seldomly improved patient outcomes. Lack of clear patient benefit and lack of data on harms and costs preclude a recommendation to adopt CCDSSs for drug therapy management.
Developers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials.
To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit.
We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information.
We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes.
Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes.
One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001).
Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.
Capturing standardized data from multiple EMRs at the point of care is highly desirable for a variety of uses, including quality improvement programs, multi-centered clinical trials and clinical decision support. In this paper, we describe the design, development and user acceptance testing of a prototype web-based form (the Form) that can integrate with multiple EMRs. We used the validated UTAUT questionnaire to assess the likelihood of uptake of the Form into clinical practice. The Form was found to be easy to use, elicits low anxiety, supports productivity and is perceived to have good support. Users would benefit from training and from better social signaling about the importance of using the Form in their practice. Making the Form more fun and interesting could help increase uptake.
Background:
To reduce physicians' inappropriate laboratory requests for their patients, administrators have used methods such as modifying a laboratory request order form with an agreed requesting protocol for the most common diagnoses in primary health care.
Objective:
To study the effects of removing the erythrocyte sedimentation rate (ESR) and aspartate transaminase (AST) which are considered of limited clinical value for primary care clinical decision-making from a computerized laboratory test order form. These tests were removed to another new view from the electronic laboratory menu where the physicians, instead of just ticking the desired test from the list, had to do 4-8s extra work by writing down the abbreviation to order the test.
Methods:
An observational controlled prospective study based on a before-after design was performed by removing AST and ES from the laboratory test order form of the computerized laboratory system for all primary care in the city of Helsinki, Finland. The numbers of annual and monthly use of AST and ESR and their controls, alanine transaminase (ALT) and C-reactive protein (CRP) ordered by General practitioners (GPs) was recorded over an eight-year period: four years before and a four years after the removal of AST and ES.
Results:
Removing AST and ESR from the computerized laboratory test order form decreased their use by up to 90%, whereas the use of the control tests increased throughout the follow-up period. The variation in use of these removed tests also decreased.
Conclusion:
Removing a laboratory test from a computerized laboratory test order form may significantly reduce GPs' use of the laboratory test. Further studies are needed, however, to ensure the safety of this type of intervention.
Clinical decision support interventions are typically heterogeneous in nature, making it difficult to identify why some interventions succeed while others do not. One approach to identify factors important to the success of health information systems is the use of meta-regression techniques, in which potential explanatory factors are correlated with the outcome of interest. This approach, however, can result in misleading conclusions due to several issues. In this manuscript, we present a cautionary case study in the context of clinical decision support systems to illustrate the limitations of this type of analysis. We then discuss implications and recommendations for future work aimed at identifying success factors of medical informatics interventions. In particular, we identify the need for head-to-head trials in which the importance of system features is directly evaluated in a prospective manner.
Copyright © 2015. Published by Elsevier Inc.
In the last decade, the clinician-patient relationship has become more of a partnership. There is growing interest in shared decision-making (SDM) in which the clinician and patient go through all phases of the decision-making process together, share treatment preferences, and reach an agreement on treatment choice. The purpose of this review is to determine the extent, quality, and consistency of the evidence about the effectiveness of SDM.
This is a systematic review of randomised controlled trials (RCTs) comparing SDM interventions with non-SDM interventions. Eleven RCTs met the required criteria, and were included in this review.
The methodological quality of the studies included in this review was high overall. Five RCTs showed no difference between SDM and control, one RCT showed no short-term effects but showed positive longer-term effects, and five RCTs reported a positive effect of SDM on outcome measures. The two studies included of people with mental healthcare problems reported a positive effect of SDM.
Despite the considerable interest in applying SDM clinically, little research regarding its effectiveness has been done to date. It has been argued that SDM is particularly suitable for long-term decisions, especially in the context of a chronic illness, and when the intervention contains more than one session. Our results show that under such circumstances, SDM can be an effective method of reaching a treatment agreement. Evidence for the effectiveness of SDM in the context of other types of decisions, or in general, is still inconclusive. Future studies of SDM should probably focus on long-term decisions.
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The perils of meta-regression to identify clinical decision support system success factors
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Com Case Study -Smart Insights Digital Marketing Advice
- Dave Chaffey
Chaffey, Dave. "Amazon.Com Case Study -Smart Insights Digital Marketing Advice". Smart Insights.
N.p., 2016. Web. 10 Nov. 2016. Retrieved Nov 10, 2016 http://www.smartinsights.com/digital-marketingstrategy/online-business-revenue-models/amazon-case-study/