“How long does it take?” A mixed methods evaluation of computer-related work in GP consultations

Hayward, James; Buckingham, Susan; Thomson, Fionagh; Milne, Heather; Sheikh, Aziz; Fernando, Bernard; Cresswell, Kathrin; Williams, Robin; Pinnock, Hilary

doi:10.14236/jhi.v22i4.95

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…All US results stand in contrast to a recent study in the UK, where the average general practitioner consultation included 9.5 minutes of face-to-face time and 3.3 minutes of EHR time. 32 The UK results are consistent with observed consultation times across six countries in Europe in 2002 of 7.6 to 15.6 minutes total (overall mean, 10.7 minutes). 33 Our results imply that US FPs spend more time working in the EHR than their European counterparts spend in the entire visit.…”

Section: Discussionsupporting

confidence: 81%

A Time-Motion Study of Primary Care Physicians’ Work in the Electronic Health Record Era

Young¹,

Burge

Kumar

et al. 2018

Fam Med

134

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BACKGROUND AND OBJECTIVES:Electronic health records (EHRs) have had mixed effects on the workflow of ambulatory primary care. In this study, we update previous research on the time required to care for patients in primary care clinics with EHRs. METHODS:We directly observed family physician (FP) attendings, residents, and their ambulatory patients in 982 visits in clinics affiliated with 10 residencies of the Residency Research Network of Texas. The FPs were purposely chosen to reflect a diversity of patient care styles. We measured total visit time, previsit chart time, face-to-face time, non-face time, out-of-hours EHR work time, and total EHR work time. RESULTS:The mean (SD) visit length was 35.8 (16.6) minutes, not counting resident precepting time. The mean time components included 2.9 (3.8) minutes working in the EHR prior to entering the room, 16.5 (9.2) minutes of faceto-face time not working in the EHR, 2.0 (2.1) minutes working in the EHR in the room (which occurred in 73.4% of the visits), 7.5 (7.5) minutes of non-face time (mostly EHR time), and 6.9 (7.6) minutes of EHR work outside of normal clinic operational hours (which occurred in 64.6% of the visits). The total time and total EHR time varied only slightly between faculty physicians, third-year and second-year residents. Multivariable linear regression analysis revealed many factors associated with total visit time including patient, physician, and clinic infrastructure factors.CONCLUSIONS: Primary care physicians spent more time working in the EHR than they spent in face-to-face time with patients in clinic visits. (Fam Med. 2018;50(2):91-9.)

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Section: Discussionsupporting

confidence: 81%

A Time-Motion Study of Primary Care Physicians’ Work in the Electronic Health Record Era

Young¹,

Burge

Kumar

et al. 2018

Fam Med

134

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“…GPs are time poor, and the variables being presented to them for recording patient information in currently available clinical software programs are numerous. The computer already imposes on the time a GP might otherwise give to the patient (Dowell et al, 2013; Haywood et al, 2015; Pearce et al, 2011), so there is a selection process occurring that meets the primary objective, that is, the care of the patient. Many GPs may not think it important to “patient care” to record the indication associated with a medication or a test in a specified area – they know why the script was given or the test ordered – and this absence limits the capacity of EMRs to produce a reliable prevalence estimate.…”

Section: Discussionmentioning

confidence: 99%

Validation of electronic medical data: Identifying diabetes prevalence in general practice

Henderson

Barnett

Ghosh³

et al. 2018

HIM J

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Background: Electronic medical records are increasingly used for research with limited external validation of their data. Objective: This study investigates the validity of electronic medical data (EMD) for estimating diabetes prevalence in general practitioner (GP) patients by comparing EMD with national Bettering the Evaluation and Care of Health (BEACH) data. Method: A "decision tree" was created using inclusion/ exclusion of pre-agreed variables to determine the probability of diabetes in absence of diagnostic label, including diagnoses (coded/free-text diabetes, polycystic ovarian syndrome , impaired glucose tolerance, impaired fasting glucose), diabetic annual cycle of care (DACC), hemoglobin (HbA1 >6.5%, and prescription (metformin, other diabetes medications). Via SQL query, cases were identified in EMD of five Illawarra and Southern Practice Network practices (30,007 active patients; from 2 years to January 2015). Patient-based Supplementary Analysis of Nominated Data (SAND) sub-studies from BEACH investigating diabetes prevalence (1172 GPs; 35,162 patients; November 2012 to February 2015) were comparison data. SAND results were adjusted for number of GP encounters per year, per patient, and then age-sex standardised to match age-sex distribution of EMD patients. Cluster-adjusted 95% confidence intervals (CIs) were calculated for both datasets. Results: EMD diabetes prevalence (T1 and/or T2) was 6.5% (95% CI: 4.1-8.9). Following age-sex standardisation, SAND prevalence, not significantly different, was 6.7% (95% CI: 6.3-7.1). Extracting only coded diagnosis missed 13.0% of probable cases, subsequently identified through the presence of metformin/other diabetes medications medications (*without other indicator variables; 6.1%), free-text diabetes label (3.8%), HbA1c result* (1.6%), DACC* (1.3%), and diabetes medications* (0.2%). Discussion: While complex, proxy variables can improve usefulness of EMD for research. Without their consideration, EMD results should be interpreted with caution. Conclusion: Enforceable, transparent data linkages in EMRs would resolve many problems with identification of diagnoses. Ongoing data quality improvement remains essential.

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“…1 Primary care physicians regularly fell behind schedule due to unpredictable operational failures that occurred within consultations, 24,47,67,72,[77][78][79] including repetitive, clinicallyirrelevant electronic alerts that distracted physicians from patients' primary clinical issues, 19,21,22,42 pushed them into dealing with other tasks, 24,33,37,38,53,78,[80][81][82][83][84] and crowded their ability to think. 24,26,38,42,46,66,80,[82][83][84][85][86][87][88][89][90][91] Nine studies portrayed interrupted consultations (manifesting as phone calls or questions from other staff) as operational failures, [92][93][94][95][96][97][98][99]…”

Section: Amplification Of Compensatory Labor By Suboptimal Task Distrmentioning

confidence: 99%

Impacts of Operational Failures on Primary Care Physicians’ Work: A Critical Interpretive Synthesis of the Literature

et al. 2020

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PURPOSE Operational failures are system-level errors in the supply of information, equipment, and materials to health care personnel. We aimed to review and synthesize the research literature to determine how operational failures in primary care affect the work of primary care physicians. METHODS We conducted a critical interpretive synthesis. We searched 7 databases for papers published in English from database inception until October 2017 for primary research of any design that addressed problems interfering with primary care physicians' work. All potentially eligible titles/abstracts were screened by 1 reviewer; 30% were subject to second screening. We conducted an iterative critique, analysis, and synthesis of included studies. RESULTS Our search retrieved 8,544 unique citations. Though no paper explicitly referred to "operational failures," we identified 95 papers that conformed to our general definition. The included studies show a gap between what physicians perceived they should be doing and what they were doing, which was strongly linked to operational failures-including those relating to technology, information, and coordination-over which physicians often had limited control. Operational failures actively configured physicians' work by requiring significant compensatory labor to deliver the goals of care. This labor was typically unaccounted for in scheduling or reward systems and had adverse consequences for physician and patient experience. CONCLUSIONS Primary care physicians' efforts to compensate for suboptimal work systems are often concealed, risking an incomplete picture of the work they do and problems they routinely face. Future research must identify which operational failures are highest impact and tractable to improvement.

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“How long does it take?” A mixed methods evaluation of computer-related work in GP consultations

Cited by 5 publications

References 32 publications

A Time-Motion Study of Primary Care Physicians’ Work in the Electronic Health Record Era

A Time-Motion Study of Primary Care Physicians’ Work in the Electronic Health Record Era

Validation of electronic medical data: Identifying diabetes prevalence in general practice

Impacts of Operational Failures on Primary Care Physicians’ Work: A Critical Interpretive Synthesis of the Literature

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