Design and implementation of electronic health record integrated clinical prediction rules (iCPR): a randomized trial in diverse primary care settings

Objectives This article describes a method for developing electronic health record (EHR) tools for use in primary care settings. Methods The “Translating Research into Agile Development” (TRIAD) method relies on the close collaboration of researchers, end users, and development teams. This five-step method for designing a tailored EHR tool includes (1) assessment, observation, and documentation; (2) structured engagement for collaboration and iterative data collection; (3) data distillation; (4) developmental feedback from clinical team members on high-priority EHR needs and input on design prototypes and EHR functionality; and (5) agile scrum sprint cycles for prototype development. Results The TRIAD method was used to modify an existing EHR for behavioral health clinicians (BHCs) embedded with primary care teams, called the BH e-Suite. The structured engagement processes stimulated discussions on how best to automate BHC screening tools and provide goal tracking functionality over time. Data distillation procedures rendered technical documents, with information on workflow steps, tasks, and associated challenges. In the developmental feedback phase, BHCs gave input on screening assessments, scoring needs, and other functionality to inform prototype feature development. Six 2-week sprint cycles were conducted to address three domains of prototype development: assessment and documentation needs, information retrieval, and monitoring and tracking. The BH e-Suite tool resulted with eight new EHR features to accommodate BHCs' needs. Conclusion The TRIAD method can be used to develop EHR functionality to address the evolving needs of health professionals in primary care and other settings. The BH e-Suite was developed through TRIAD and was found to be acceptable, easy to use, and improved care delivery during pilot testing. The BH e-Suite was later adopted by OCHIN Inc., which provided the tool to its 640 community health centers. This suggests that the TRIAD method is a promising research and development approach.

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Use of electronic health record data to identify skin and soft tissue infections in primary care settings: a validation study

BMC Infectious Diseases ◽

10.1186/1471-2334-13-171 ◽

2013 ◽

Vol 13 (1) ◽

Cited By ~ 16

Author(s):

Pamela J Levine ◽

Miriam R Elman ◽

Ravina Kullar ◽

John M Townes ◽

David T Bearden ◽

...

Keyword(s):

Primary Care ◽

Soft Tissue ◽

Electronic Health Record ◽

Validation Study ◽

Health Record ◽

Soft Tissue Infections ◽

Electronic Health Record Data ◽

Primary Care Settings ◽

Record Data ◽

Electronic Health

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Digital Health Interventions to Enhance Prevention in Primary Care: A Scoping Review (Preprint)

10.2196/preprints.33518 ◽

2021 ◽

Author(s):

Van C Willis ◽

Kelly Jean Thomas Craig ◽

Yalda Yabbarpour ◽

Elisabeth L Scheufele ◽

Yull E Arriaga ◽

...

Keyword(s):

Primary Care ◽

Electronic Health Record ◽

Scoping Review ◽

Subgroup Analysis ◽

Care Management ◽

Digital Health ◽

Health Interventions ◽

Health Record ◽

Primary Care Settings ◽

Electronic Health

BACKGROUND Digital transformation of primary care practices, including the use digital health interventions (DHIs), has yet to be systematically evaluated. OBJECTIVE To identify and describe the scope and use of current DHIs for preventive care in primary care settings. METHODS A scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and MeSH terms covering primary care professionals AND prevention and care management AND digital health. A subgroup analysis identified relevant studies conducted in US primary care settings excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (e.g., healthcare performance and implementation science), and study quality as per Oxford Levels of Evidence were abstracted. RESULTS The search yielded 5,274 citations of which 1,060 full-texts were identified. Following a subgroup analysis, 241 articles met inclusion criteria. Studies primarily examined DHIs among health information technology including EHRs (69%), clinical decision support (41%), telehealth (37%), or multiple technologies (61%). DHIs were predominantly used for tertiary prevention (55%). Of the core primary care functions, comprehensiveness was addressed most frequently (87%). DHI users were providers (85%), patients (46%), or multiples (37%). Reported outcomes were primarily clinical (70%) and statistically significant improvements were common (69%). Results were summarized across five topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. Quality of the included studies was moderate-to-low. CONCLUSIONS Preventive DHIs used in primary care settings demonstrated meaningful improvements in both clinical and non-clinical outcomes across user types; however, adoption and implementation in the US was limited to primarily electronic health record-centric platforms and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluation of negative results, effects on health disparities, and many other gaps remain to be explored.

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Variability in Electronic Health Record Use for Medication Management in Primary Care (Preprint)

10.2196/preprints.10289 ◽

2018 ◽

Author(s):

Enid Montague

Keyword(s):

Primary Care ◽

Electronic Health Record ◽

Review Process ◽

Medication Management ◽

Controlled Environment ◽

Health Record ◽

Coding Scheme ◽

Primary Care Settings ◽

Electronic Health ◽

Acute Condition

UNSTRUCTURED Background: Traditional medication management complexity now combined with EHR systems, which are still novel in primary care settings, propose new challenges in trying to improve physician workflow. Objective: The purpose of this study was to understand how workflow variability in medication management using an electronic health record (EHR) system is related to patient and physician factors. Methods: Two different patient cases (chronic vs. acute condition) were presented to participants in a controlled environment. A task action coding scheme was used to analyze the videotaped data from the physician’s EHR usage. A usability survey was administered after the task. Results: High variability in the medication review process and EHR perceptions were revealed. Patient conditions and physicians’ EHR perceptions were related to the found variability in the workflow. Conclusions: Interventions designed to improve EHR medication management in primary care should consider alignment with physician’s varied workflow linked with their perceptions and differing patient condition.

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Design of a cluster-randomized trial of electronic health record-based tools to address overweight and obesity in primary care

Clinical Trials ◽

10.1177/1740774515578132 ◽

2015 ◽

Vol 12 (4) ◽

pp. 374-383 ◽

Cited By ~ 6

Author(s):

Heather J Baer ◽

Christina C Wee ◽

Katerina DeVito ◽

E John Orav ◽

Joseph P Frolkis ◽

...

Keyword(s):

Primary Care ◽

Electronic Health Record ◽

Randomized Trial ◽

Cluster Randomized Trial ◽

Overweight And Obesity ◽

Health Record ◽

Electronic Health ◽

Cluster Randomized

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Use of certain electronic health record features is associated with improved primary care quality measures

PsycEXTRA Dataset ◽

10.1037/e667672010-010 ◽

2010 ◽

Keyword(s):

Primary Care ◽

Electronic Health Record ◽

Quality Measures ◽

Care Quality ◽

Health Record ◽

Primary Care Quality ◽

Electronic Health

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Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

BMJ Open ◽

10.1136/bmjopen-2020-037405 ◽

2020 ◽

Vol 10 (10) ◽

pp. e037405

Author(s):

Daniel Dedman ◽

Melissa Cabecinha ◽

Rachael Williams ◽

Stephen J W Evans ◽

Krishnan Bhaskaran ◽

...

Keyword(s):

Systematic Review ◽

Primary Care ◽

Electronic Health Record ◽

Observational Studies ◽

Meta Analysis ◽

Data Sources ◽

Health Record ◽

Two Stage ◽

One Stage ◽

Electronic Health

ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

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