Dataset Information

Validity of Medical Record Abstraction and Electronic Health Record-Generated Reports to Assess Performance on Cardiovascular Quality Measures in Primary Care.

ABSTRACT: 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.

SUBMITTER: Homco J

PROVIDER: S-EPMC7388024 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Validity of Medical Record Abstraction and Electronic Health Record-Generated Reports to Assess Performance on Cardiovascular Quality Measures in Primary Care.

Homco Juell J Carabin Hélène H Nagykaldi Zsolt Z Garwe Tabitha T Duffy F Daniel FD Kendrick David D Martinez Sydney S Zhao Yan Daniel YD Stoner Julie J

JAMA network open 20200701 7

<h4>Importance</h4>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).<h4>Objective</h4>To compare observed performance scores measured using 2 imperfect reference standard data sources ( ...[more]

PMID: 32721028

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Project description:Background:Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenemia, oligo-anovulation, and numerous ovarian cysts. Hospital electronic medical records provide an avenue for investigating polycystic ovary morphology commonly seen in PCOS at a large scale. The purpose of this study was to develop and evaluate the performance of two machine learning text algorithms, for classification of polycystic ovary morphology (PCOM) in pelvic ultrasounds. Methods:Pelvic ultrasound reports from patients at Boston Medical Center between October 1, 2003 and December 12, 2016 were included for analysis, which resulted in 39,093 ultrasound reports from 25,535 unique women. Following the 2003 Rotterdam Consensus Criteria for polycystic ovary syndrome, 2000 randomly selected ultrasounds were expert labeled for PCOM status as present, absent, or unidentifiable (not able to be determined from text alone). An ovary was marked as having PCOM if there was mention of numerous peripheral follicles or if the volume was greater than 10?ml in the absence of a dominant follicle or other confounding pathology. Half of the labeled data was used to develop and refine the algorithms, and the other half was used as a test set for evaluating its accuracy. Results:On the evaluation set of 1000 random US reports, the accuracy of the classifiers were 97.6% (95% CI: 96.5, 98.5%) and 96.1% (94.7, 97.2%). Both models were more adept at identifying PCOM-absent ultrasounds than either PCOM-unidentifiable or PCOM-present ultrasounds. The two classifiers estimated prevalence of PCOM within the whole set of 39,093 ultrasounds to be 44% PCOM-absent, 32% PCOM-unidentifiable, and 24% PCOM-present. Conclusions:Although accuracy measured on the test set and inter-rater agreement between the two classifiers (Cohen's Kappa?=?0.988) was high, a major limitation of our approach is that it uses the ultrasound report text as a proxy and does not directly count follicles from the ultrasound images themselves.

Project description:This study investigates risk of mortality associated with nurses' assessments of patients by physiological system. We hypothesise that nursing assessments of in-patients performed at entry correlate with in-hospital mortality, and those performed just before discharge correlate with postdischarge mortality. Cohort study of in-hospital and postdischarge mortality of patients over two 1-year periods. An 805-bed community hospital in Sarasota, Florida, USA. 42 302 inpatients admitted for any reason, excluding obstetrics, paediatric and psychiatric patients. All-cause mortalities and mortality OR. Patients whose entry nursing assessments, other than pain, did not meet minimum standards had significantly higher in-hospital mortality than patients meeting minimums; and final nursing assessments before discharge had large OR for postdischarge mortality. In-hospital mortality OR were found to be: food, 7.0; neurological, 9.4; musculoskeletal, 6.9; safety, 5.6; psychosocial, 6.7; respiratory, 8.1; skin, 5.2; genitourinary, 3.0; gastrointestinal, 2.3; peripheral-vascular, 3.9; cardiac, 2.8; and pain, 1.1. CI at 95% are within ±20% of these values, with p<0.001 (except for pain). Similar results applied to postdischarge mortality. All results were comparable across the two 1-year periods, with 0.85 intraclass correlation coefficient. Nursing assessments are strongly correlated with in-hospital and postdischarge mortality. No multivariate analysis has yet been performed, and will be the subject of a future study, thus there may be confounding factors. Nonetheless, we conclude that these assessments are clinically meaningful and valid. Nursing assessment data, which are currently unused, may allow physicians to improve patient care. The mortality OR and the dynamic nature of nursing assessments suggest that nursing assessments are sensitive indicators of a patient's condition. While these conclusions must remain qualified, pending future multivariate analyses, nursing assessment data ought to be incorporated in risk-related health research, and changes in record-keeping software are needed to make this information more accessible.

Dataset Information

Validity of Medical Record Abstraction and Electronic Health Record-Generated Reports to Assess Performance on Cardiovascular Quality Measures in Primary Care.

Publications

Validity of Medical Record Abstraction and Electronic Health Record-Generated Reports to Assess Performance on Cardiovascular Quality Measures in Primary Care.

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