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Method of electronic health record documentation and quality of primary care.

ABSTRACT: Physicians who more intensively interact with electronic health records (EHRs) through their documentation style may pay greater attention to coded fields and clinical decision support and thus may deliver higher quality care. We measured the quality of care of physicians who used three predominating EHR documentation styles: dictation, structured documentation, and free text.We conducted a retrospective analysis of visits by patients with coronary artery disease and diabetes to the Partners Primary Care Practice Based Research Network. The main outcome measures were 15 EHR-based coronary artery disease and diabetes measures assessed 30 days after primary care visits.During the 9-month study period, 7000 coronary artery disease and diabetes patients made 18?569 visits to 234 primary care physicians of whom 20 (9%) predominantly dictated their notes, 68 (29%) predominantly used structured documentation, and 146 (62%) predominantly typed free text notes. In multivariable modeling adjusted for clustering by patient and physician, quality of care appeared significantly worse for dictators than for physicians using the other two documentation styles on three of 15 measures (antiplatelet medication, tobacco use documentation, and diabetic eye exam); better for structured documenters for three measures (blood pressure documentation, body mass index documentation, and diabetic foot exam); and better for free text documenters on one measure (influenza vaccination). There was no measure for which dictators had higher quality of care than physicians using the other two documentation styles.EHR-assessed quality is necessarily documentation-dependent, but physicians who dictated their notes appeared to have worse quality of care than physicians who used structured EHR documentation.ClinicalTrials.gov Identifier: NCT00235040.

SUBMITTER: Linder JA

PROVIDER: S-EPMC3534457 | biostudies-literature | 2012 Nov-Dec

REPOSITORIES: biostudies-literature

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Method of electronic health record documentation and quality of primary care.

Linder Jeffrey A JA Schnipper Jeffrey L JL Middleton Blackford B

Journal of the American Medical Informatics Association : JAMIA 20120519 6

<h4>Objective</h4>Physicians who more intensively interact with electronic health records (EHRs) through their documentation style may pay greater attention to coded fields and clinical decision support and thus may deliver higher quality care. We measured the quality of care of physicians who used three predominating EHR documentation styles: dictation, structured documentation, and free text.<h4>Methods</h4>We conducted a retrospective analysis of visits by patients with coronary artery diseas ...[more]

PMID: 22610494

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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:ImportanceElectronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD).ObjectiveTo evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria.Design, setting, and participantsThis retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020.Main outcomes and measuresThe primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system.ResultsAmong the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes.Conclusions and relevanceThese results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.

Project description:Importance:Cardiovascular disease is the leading cause of death in the United States. To improve cardiovascular outcomes, primary care must have valid methods of assessing performance on cardiovascular clinical quality measures, including aspirin use (aspirin measure), blood pressure control (BP measure), and smoking cessation counseling and intervention (smoking measure). Objective:To compare observed performance scores measured using 2 imperfect reference standard data sources (medical record abstraction [MRA] and electronic health record [EHR]-generated reports) with misclassification-adjusted performance scores obtained using bayesian latent class analysis. Design, Setting, and Participants:This cross-sectional study used a subset of the 2016 aspirin, BP, and smoking performance data from the Healthy Hearts for Oklahoma Project. Each clinical quality measure was calculated for a subset of a practice's patient population who can benefit from recommended care (ie, the eligible population). A random sample of 380 eligible patients were included for the aspirin measure; 126, for the BP measure; and 115, for the smoking measure. Data were collected from 21 primary care practices belonging to a single large health care system from January 1 to December 31, 2018, and analyzed from February 21 to April 17, 2019. Main Outcomes and Measures:The main outcomes include performance scores for the aspirin, BP, and smoking measures using imperfect MRA and EHRs and estimated through bayesian latent class models. Results:A total of 621 eligible patients were included in the analysis. Based on MRA and EHR data, observed aspirin performance scores were 76.0% (95% bayesian credible interval [BCI], 71.5%-80.1%) and 74.9% (95% BCI, 70.4%-79.1%), respectively; observed BP performance scores, 80.6% (95% BCI, 73.2%-86.9%) and 75.1% (95% BCI, 67.2%-82.1%), respectively; and observed smoking performance scores, 85.7% (95% BCI, 78.6%-91.2%) and 75.4% (95% BCI, 67.0%-82.6%), respectively. Misclassification-adjusted estimates were 74.9% (95% BCI, 70.5%-79.1%) for the aspirin performance score, 75.0% (95% BCI, 66.6%-82.5%) for the BP performance score, and 83.0% (95% BCI, 74.4%-89.8%) for the smoking performance score. Conclusions and Relevance:Ensuring valid performance measurement is critical for value-based payment models and quality improvement activities in primary care. This study found that extracting information for the same individuals using different data sources generated different performance score estimates. Further research is required to identify the sources of these differences.

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.

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Method of electronic health record documentation and quality of primary care.

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Method of electronic health record documentation and quality of primary care.

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