Abstract:Objective
To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations.
Materials and Methods
We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; d… Show more
“…These goals are conceptually obvious but difficult to achieve in practice. For example, a patient’s data is often distributed across “archipelagos” of EHR sources, which may not adhere to interoperability standards [ 90 ]—greater collaboration between EHR vendors and developers is needed to enable a greater variety of EHR data to be extracted, which would increase CDS algorithm complexity and improve clinician workflow [ 95 , 97 , 98 ].…”
Background
Clinical decision support (CDS) is increasingly used to facilitate chronic disease care. Despite increased availability of electronic health records and the ongoing development of new CDS technologies, uptake of CDS into routine clinical settings is inconsistent. This qualitative systematic review seeks to synthesise healthcare provider experiences of CDS—exploring the barriers and enablers to implementing, using, evaluating, and sustaining chronic disease CDS systems.
Methods
A search was conducted in Medline, CINAHL, APA PsychInfo, EconLit, and Web of Science from 2011 to 2021. Primary research studies incorporating qualitative findings were included if they targeted healthcare providers and studied a relevant chronic disease CDS intervention. Relevant CDS interventions were electronic health record-based and addressed one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolaemia. Qualitative findings were synthesised using a meta-aggregative approach.
Results
Thirty-three primary research articles were included in this qualitative systematic review. Meta-aggregation of qualitative data revealed 177 findings and 29 categories, which were aggregated into 8 synthesised findings. The synthesised findings related to clinical context, user, external context, and technical factors affecting CDS uptake. Key barriers to uptake included CDS systems that were simplistic, had limited clinical applicability in multimorbidity, and integrated poorly into existing workflows. Enablers to successful CDS interventions included perceived usefulness in providing relevant clinical knowledge and structured chronic disease care; user confidence gained through training and post training follow-up; external contexts comprised of strong clinical champions, allocated personnel, and technical support; and CDS technical features that are both highly functional, and attractive.
Conclusion
This systematic review explored healthcare provider experiences, focussing on barriers and enablers to CDS use for chronic diseases. The results provide an evidence-base for designing, implementing, and sustaining future CDS systems. Based on the findings from this review, we highlight actionable steps for practice and future research.
Trial registration
PROSPERO CRD42020203716
“…These goals are conceptually obvious but difficult to achieve in practice. For example, a patient’s data is often distributed across “archipelagos” of EHR sources, which may not adhere to interoperability standards [ 90 ]—greater collaboration between EHR vendors and developers is needed to enable a greater variety of EHR data to be extracted, which would increase CDS algorithm complexity and improve clinician workflow [ 95 , 97 , 98 ].…”
Background
Clinical decision support (CDS) is increasingly used to facilitate chronic disease care. Despite increased availability of electronic health records and the ongoing development of new CDS technologies, uptake of CDS into routine clinical settings is inconsistent. This qualitative systematic review seeks to synthesise healthcare provider experiences of CDS—exploring the barriers and enablers to implementing, using, evaluating, and sustaining chronic disease CDS systems.
Methods
A search was conducted in Medline, CINAHL, APA PsychInfo, EconLit, and Web of Science from 2011 to 2021. Primary research studies incorporating qualitative findings were included if they targeted healthcare providers and studied a relevant chronic disease CDS intervention. Relevant CDS interventions were electronic health record-based and addressed one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolaemia. Qualitative findings were synthesised using a meta-aggregative approach.
Results
Thirty-three primary research articles were included in this qualitative systematic review. Meta-aggregation of qualitative data revealed 177 findings and 29 categories, which were aggregated into 8 synthesised findings. The synthesised findings related to clinical context, user, external context, and technical factors affecting CDS uptake. Key barriers to uptake included CDS systems that were simplistic, had limited clinical applicability in multimorbidity, and integrated poorly into existing workflows. Enablers to successful CDS interventions included perceived usefulness in providing relevant clinical knowledge and structured chronic disease care; user confidence gained through training and post training follow-up; external contexts comprised of strong clinical champions, allocated personnel, and technical support; and CDS technical features that are both highly functional, and attractive.
Conclusion
This systematic review explored healthcare provider experiences, focussing on barriers and enablers to CDS use for chronic diseases. The results provide an evidence-base for designing, implementing, and sustaining future CDS systems. Based on the findings from this review, we highlight actionable steps for practice and future research.
Trial registration
PROSPERO CRD42020203716
“… 19 For example, this Baseline Approach was used by UU Health to generate care reminders and to support shared decision-making for lung cancer screening. 19 , 23 Logic of the Baseline Approach The Baseline Approach is as follows: Pack-years are calculated by multiplying the years-smoked and packs-per-day most recently recorded in the EHR. For former smokers, years-quit are calculated as the years that have passed since the quit date most recently recorded in the EHR.…”
Objective
The US Preventive Services Task Force (USPSTF) requires the estimation of lifetime pack-years to determine lung cancer screening eligibility. Leading electronic health record (EHR) vendors calculate pack-years using only the most recently recorded smoking data. The objective was to characterize EHR smoking data issues and to propose an approach to addressing these issues using longitudinal smoking data.
Materials and Methods
In this cross-sectional study, we evaluated 16 874 current or former smokers who met USPSTF age criteria for screening (50–80 years old), had no prior lung cancer diagnosis, and were seen in 2020 at an academic health system using the Epic® EHR. We described and quantified issues in the smoking data. We then estimated how many additional potentially eligible patients could be identified using longitudinal data. The approach was verified through manual review of records from 100 subjects.
Results
Over 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%). Addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screening (P < .001).
Discussion
Missing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening. Data collection and analysis strategies that reflect changes in smoking habits over time could improve the identification of patients eligible for screening.
Conclusion
The use of longitudinal EHR smoking data could improve lung cancer screening.
“…They therefore fall short when addressing complex care needs spanning multiple providers and disciplines. Just like care fragmentation, technological fragmentation generates frustration and dropout for both patients and caregivers (Kawamoto et al. , 2021).…”
PurposeThis article aims to describe the thinking behind MASSE, a project in Finland that helps address the fragmentation of care and patient journey disruptions for long-term care. It outlines the conceptualization of an information technology (IT)-assisted solution and presents preliminary findings and research problems in this ongoing project.Design/methodology/approachThe project employs a service engineering and design science approach with the objective of addressing chronic and multimorbid patients in specialized multiprovider environments. It does this by applying information and communication technologies and organizational design. The project has been a cocreative effort with ongoing interviews and workshops with various stakeholders to inform the conceptualization of a solution, an intermediary step before the implementation phase.FindingsPatient journey disruptions occur when caregivers do not know what to do in specific situations. A potential solution is a virtual care operator (VCO) with a personalized patient card that would enable service ecosystem actors to integrate and coordinate their tasks. This article presents the basic design principles of such a solution.Research limitations/implicationsConceptual ideas and preliminary results only indicative.Practical implicationsSystemic integration efforts like those ongoing in Finland can benefit from the VCO concept encouraging a more collaborative way of thinking about integrative solutions and opening up new avenues of research on business implications and ecosystem strategies.Social implicationsThe VCO concept answers to the continuity of care, the rising costs of health care and the growing numbers of patients with chronic disease and multimorbidity whose care remains fragmented and uncoordinated.Originality/valueTaking an ecosystem approach to care integration and addressing interoperability issues are on the cutting edge of healthcare system transformation.
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