Dataset Information

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.

ABSTRACT: BACKGROUND:Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms. METHODS:We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category. RESULTS:A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37?±?1.38. Specifically, the average knowledge (K) score is 0.64?±?0.66, interpretation (I) score is 0.33?±?0.55, and programming (P) score is 0.40?±?0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks. CONCLUSION:This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.

SUBMITTER: Shang N

PROVIDER: S-EPMC6894517 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.

Shang Ning N Liu Cong C Rasmussen Luke V LV Ta Casey N CN Caroll Robert J RJ Benoit Barbara B Lingren Todd T Dikilitas Ozan O Mentch Frank D FD Carrell David S DS Wei Wei-Qi WQ Luo Yuan Y Gainer Vivian S VS Kullo Iftikhar J IJ Pacheco Jennifer A JA Hakonarson Hakon H Walunas Theresa L TL Denny Joshua C JC Wiley Ken K Murphy Shawn N SN Hripcsak George G Weng Chunhua C

Journal of biomedical informatics 20190919

<h4>Background</h4>Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms.<h4>Methods</h4>We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records ...[more]

PMID: 31542521

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Project description:BackgroundMany large-scale cardiovascular clinical trials are plagued with escalating costs and low enrollment. Implementing a computable phenotype, which is a set of executable algorithms, to identify a group of clinical characteristics derivable from electronic health records or administrative claims records, is essential to successful recruitment in large-scale pragmatic clinical trials. This methods paper provides an overview of the development and implementation of a computable phenotype in ADAPTABLE (Aspirin Dosing: a Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness)-a pragmatic, randomized, open-label clinical trial testing the optimal dose of aspirin for secondary prevention of atherosclerotic cardiovascular disease events.Methods and resultsA multidisciplinary team developed and tested the computable phenotype to identify adults ≥18 years of age with a history of atherosclerotic cardiovascular disease without safety concerns around using aspirin and meeting trial eligibility criteria. Using the computable phenotype, investigators identified over 650 000 potentially eligible patients from the 40 participating sites from Patient-Centered Outcomes Research Network-a network of Clinical Data Research Networks, Patient-Powered Research Networks, and Health Plan Research Networks. Leveraging diverse recruitment methods, sites enrolled 15 076 participants from April 2016 to June 2019. During the process of developing and implementing the ADAPTABLE computable phenotype, several key lessons were learned. The accuracy and utility of a computable phenotype are dependent on the quality of the source data, which can be variable even with a common data model. Local validation and modification were required based on site factors, such as recruitment strategies, data quality, and local coding patterns. Sustained collaboration among a diverse team of researchers is needed during computable phenotype development and implementation.ConclusionsThe ADAPTABLE computable phenotype served as an efficient method to recruit patients in a multisite pragmatic clinical trial. This process of development and implementation will be informative for future large-scale, pragmatic clinical trials. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02697916.

Project description:ObjectiveTo describe the development of the Pediatric Epilepsy Outcome-Informatics Project (PEOIP) at Alberta Children's Hospital (ACH), which was created to provide standardized, point-of-care data entry; near-time data analysis; and availability of outcome dashboards as a baseline on which to pursue quality improvement.MethodsStakeholders involved in the PEOIP met weekly to determine the most important outcomes for patients diagnosed with epilepsy, create a standardized electronic note with defined fields (patient demographics, seizure and syndrome type and frequency and specific outcomes- seizure type and frequency, adverse effects, emergency department visits, hospitalization, and care pathways for clinical decision support. These were embedded in the electronic health record from which the fields were extracted into a data display platform that provided patient- and population-level dashboards updated every 36 hours. Provider satisfaction and family experience surveys were performed to assess the impact of the standardized electronic note.ResultsIn the last 5 years, 3,245 unique patients involving 13, 831 encounters had prospective, longitudinal, standardized epilepsy data accrued via point-of-care data entry into an electronic note as part of routine clinical care. A provider satisfaction survey of the small number of users involved indicated that the vast majority believed that the note makes documentation more efficient. A family experience survey indicated that being provided with the note was considered "valuable" or "really valuable" by 86% of respondents and facilitated communication with family members, school, and advocacy organizations.SignificanceThe PEOIP serves as a proof of principle that information obtained as part of routine clinical care can be collected in a prospective, standardized, efficient manner and be used to construct filterable process/outcome dashboards, updated in near time (36 hours). This information will provide the necessary baseline data on which multiple of QI projects to improve meaningful outcomes for children with epilepsy will be based.

Project description:BackgroundThis study was a retrospective evaluation of an unsuccessfully implemented team-based, chronic disease management program, with an aim to understand more about implementation barriers. The program, the Chronic Disease Management Initiative (CDMI) was a new collaborative model of care for patients with COPD. It utilized customized health information and interactive tools, mainly smartphones, for ongoing disease management. The program's goal was to demonstrate that integrated team-based models of care could improve patient care, as well as reduce readmission rates and emergency department visits. The program planning for CDMI began in 2017, followed by the implementation and evaluation period in 2018. After a 10-month implementation period, the program was unable to enroll a sufficient number of patients to examine if there was an improvement in patient outcomes.MethodsA retrospective case-study design using multiple data sources was used to gather feedback from participants involved in CDMI. Data collection occurred throughout planning and implementation and continued into early 2019. Semi-structured interviews were conducted, and transcripts were analyzed using NVivo 10 software. This was followed by content analysis.ResultsAnalysis revealed four key themes as barriers to CDMI's implementation: 1) lack of a needs assessment with key stakeholders; 2) lack of buy-in from medical staff; 3) inadequate patient engagement and; 4) contextual barriers. Planners did not conduct a proper needs assessment, nor include patients in the study design. In addition, there was insufficient consideration for how CDMI should be integrated into the usual COPD care plan, leading to confusion in roles and responsibilities. Poor communication between the implementation team and healthcare providers implementing the program, led to a lack of buy-in and engagement.ConclusionThe key themes resonate with what is already known in the literature. This study supports the importance of using a theoretically grounded plan for implementation. Using a model only in the planning stages is insufficient even when an intervention is based on evidence to support higher quality care. It is imperative to consider practical and contextual factors of program implementation and their interactions. By detailing the 'failed implementation' of this intervention, we hope to share important lessons about the need to plan implementation processes early in program planning.

Dataset Information

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.

Publications

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.

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