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A typology of electronic health record workarounds in small-to-medium size primary care practices.

ABSTRACT: Electronic health record (EHR) use in ambulatory care can improve safety and quality; however, problems with design, implementation, and poor interface with other systems lead users to develop 'workarounds', or behaviors users adopt to overcome perceived limitations in a technical system. We documented workarounds used in independent, community-based primary care practices, and developed a typology of their key features.Comparative case study of EHR use in seven independent primary care practices. Field researchers spent approximately 1 month in each practice to observe EHR use, conduct patient pathways, and interview clinicians and staff.We observed workarounds addressing a wide range of EHR-related problems, including: user interface issues (eg, insufficient data fields, limited templates), barriers to electronic health information exchange with external organizations, and struggles incorporating new technologies into existing office space. We analyzed the observed workarounds inductively to develop a typology that cuts across specific clinical or administrative processes to highlight the following key formal features of workarounds in general: temporary/routinized, which captures whether the workaround is taken for granted as part of daily workflow or is understood as a short-term solution; avoidable/unavoidable, referring to the extent to which the workaround is within the practice's power to eliminate; and deliberately chosen/unplanned, which differentiates strategically chosen adaptations from less thoughtful workarounds.This workaround typology provides a framework for EHR users to identify and address workarounds in their own practices, and for researchers to examine the effect of different types of EHR workarounds on patient safety, care quality, and efficiency.

SUBMITTER: Friedman A

PROVIDER: S-EPMC3957384 | biostudies-other | 2014 Feb

REPOSITORIES: biostudies-other

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A typology of electronic health record workarounds in small-to-medium size primary care practices.

Friedman Asia A Crosson Jesse C JC Howard Jenna J Clark Elizabeth C EC Pellerano Maria M Karsh Ben-Tzion BT Crabtree Benjamin B Jaén Carlos Roberto CR Cohen Deborah J DJ

Journal of the American Medical Informatics Association : JAMIA 20130731 e1

<h4>Objective</h4>Electronic health record (EHR) use in ambulatory care can improve safety and quality; however, problems with design, implementation, and poor interface with other systems lead users to develop 'workarounds', or behaviors users adopt to overcome perceived limitations in a technical system. We documented workarounds used in independent, community-based primary care practices, and developed a typology of their key features.<h4>Materials and methods</h4>Comparative case study of EH ...[more]

PMID: 23904322

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Project description:BACKGROUND:The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. METHODS:Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. RESULTS:A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). CONCLUSIONS:This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

Project description:OBJECTIVE:Analysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development. DESIGN:We describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an 'uncertainty' variable to allow sensitivity analysis. SETTING:These methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software. PARTICIPANTS:The codelist was used to estimate the frequency of shortness of breath in a cohort of 28?216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD). RESULTS:Of 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13?091 episodes of shortness of breath were identified in the cohort of 28?216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice. CONCLUSIONS:Although initially time consuming, using a rigorous and reproducible method for codelist generation 'future-proofs' findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including: definitions and justifications associated with each codelist; the syntax or search method; the number of candidate codes identified; and the categorisation of codes after Delphi review.

Project description:Backgroundfrailty is an especially problematic expression of population ageing. International guidelines recommend routine identification of frailty to provide evidence-based treatment, but currently available tools require additional resource.Objectivesto develop and validate an electronic frailty index (eFI) using routinely available primary care electronic health record data.Study design and settingretrospective cohort study. Development and internal validation cohorts were established using a randomly split sample of the ResearchOne primary care database. External validation cohort established using THIN database.Participantspatients aged 65-95, registered with a ResearchOne or THIN practice on 14 October 2008.Predictorswe constructed the eFI using the cumulative deficit frailty model as our theoretical framework. The eFI score is calculated by the presence or absence of individual deficits as a proportion of the total possible. Categories of fit, mild, moderate and severe frailty were defined using population quartiles.Outcomesoutcomes were 1-, 3- and 5-year mortality, hospitalisation and nursing home admission.Statistical analysishazard ratios (HRs) were estimated using bivariate and multivariate Cox regression analyses. Discrimination was assessed using receiver operating characteristic (ROC) curves. Calibration was assessed using pseudo-R(2) estimates.Resultswe include data from a total of 931,541 patients. The eFI incorporates 36 deficits constructed using 2,171 CTV3 codes. One-year adjusted HR for mortality was 1.92 (95% CI 1.81-2.04) for mild frailty, 3.10 (95% CI 2.91-3.31) for moderate frailty and 4.52 (95% CI 4.16-4.91) for severe frailty. Corresponding estimates for hospitalisation were 1.93 (95% CI 1.86-2.01), 3.04 (95% CI 2.90-3.19) and 4.73 (95% CI 4.43-5.06) and for nursing home admission were 1.89 (95% CI 1.63-2.15), 3.19 (95% CI 2.73-3.73) and 4.76 (95% CI 3.92-5.77), with good to moderate discrimination but low calibration estimates.Conclusionsthe eFI uses routine data to identify older people with mild, moderate and severe frailty, with robust predictive validity for outcomes of mortality, hospitalisation and nursing home admission. Routine implementation of the eFI could enable delivery of evidence-based interventions to improve outcomes for this vulnerable group.

Dataset Information

A typology of electronic health record workarounds in small-to-medium size primary care practices.

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A typology of electronic health record workarounds in small-to-medium size primary care practices.

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