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The effect of number of healthcare visits on study sample selection in electronic health record data.

ABSTRACT: Introduction:Few studies have addressed how to select a study sample when using electronic health record (EHR) data. Objective:To examine how changing criterion for number of visits in EHR data required for inclusion in a study sample would impact one basic epidemiologic measure: estimates of disease period prevalence. Methods:Year 2016 EHR data from three Midwestern health systems (Northwestern Medicine in Illinois, University of Iowa Health Care, and Froedtert & the Medical College of Wisconsin, all regional tertiary health care systems including hospitals and clinics) was used to examine how alternate definitions of the study sample, based on number of healthcare visits in one year, affected measures of disease period prevalence. In 2016, each of these health systems saw between 160,000 and 420,000 unique patients. Curated collections of ICD-9, ICD-10, and SNOMED codes (from CMS-approved electronic clinical quality measures) were used to define three diseases: acute myocardial infarction, asthma, and diabetic nephropathy). Results:Across all health systems, increasing the minimum required number of visits to be included in the study sample monotonically increased crude period prevalence estimates. The rate at which prevalence estimates increased with number of visits varied across sites and across diseases. Conclusions:In addition to providing thorough descriptions of case definitions, when using EHR data authors must carefully describe how a study sample is identified and report data for a range of sample definitions, including minimum number of visits, so that others can assess the sensitivity of reported results to sample definition in EHR data.

SUBMITTER: Rasmussen-Torvik LJ

PROVIDER: S-EPMC7448749 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The effect of number of healthcare visits on study sample selection in electronic health record data.

Rasmussen-Torvik Laura J LJ Furmanchuk Al'ona A Stoddard Alexander J AJ Osinski Kristen I KI Meurer John R JR Smith Nicholas N Chrischilles Elizabeth E Black Bernard S BS Kho Abel A

International journal of population data science 20200402 1

<h4>Introduction</h4>Few studies have addressed how to select a study sample when using electronic health record (EHR) data.<h4>Objective</h4>To examine how changing criterion for number of visits in EHR data required for inclusion in a study sample would impact one basic epidemiologic measure: estimates of disease period prevalence.<h4>Methods</h4>Year 2016 EHR data from three Midwestern health systems (Northwestern Medicine in Illinois, University of Iowa Health Care, and Froedtert & the Medic ...[more]

PMID: 32864475

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Project description:BackgroundMultiple policy initiatives encourage more cautious prescribing of opioids in light of their risks. Electronic health record (EHR) redesign can influence prescriber choices, but some redesigns add to workload.ObjectiveTo estimate the effect of an EHR prescribing redesign on both opioid prescribing choices and keystrokes.DesignQuality improvement quasi-experiment, analyzed as interrupted time series.ParticipantsAdult patients of an academic multispecialty practice and a federally qualified health center (FQHC) who received new prescriptions for short-acting opioids, and their providers.InterventionIn the redesign, new prescriptions of short-acting opioids defaulted to the CDC-recommended minimum for opioid-naïve patients, with no alerts or hard stops, such that 9 keystrokes were required for a guideline-concordant prescription and 24 for a non-concordant prescription.Main measuresProportion of guideline-concordant prescriptions, defined as new prescriptions with a 3-day supply or less, calculated per 2-week period. Number of mouse clicks and keystrokes needed to place prescriptions.Key resultsAcross the 2 sites, 22,113 patients received a new short-acting opioid prescription from 821 providers. Before the intervention, both settings showed secular trends toward smaller-quantity prescriptions. At the academic practice, the intervention was associated with an immediate increase in guideline-concordant prescriptions from an average of 12% to 31% of all prescriptions. At the FQHC, about 44% of prescriptions were concordant at the time of the intervention, which was not associated with an additional significant increase. However, total keystrokes needed to place the concordant prescriptions decreased 62.7% from 3552 in the 6 months before the intervention to 1323 in the 6 months afterwards.ConclusionsAutocompleting prescription forms with guideline-recommended values was associated with a large increase in guideline concordance in an organization where baseline concordance was low, but not in an organization where it was already high. The redesign markedly reduced the number of keystrokes needed to place orders, with important implications for EHR-related stress.Trial registrationwww.ClinicalTrials.gov protocol 1710018646.

Project description:ObjectivesRecent sepsis studies have defined patients as "infected" using a combination of culture and antibiotic orders rather than billing data. However, the accuracy of these definitions is unclear. We aimed to compare the accuracy of different established criteria for identifying infected patients using detailed chart review.DesignRetrospective observational study.SettingSix hospitals from three health systems in Illinois.PatientsAdult admissions with blood culture or antibiotic orders, or Angus International Classification of Diseases infection codes and death were eligible for study inclusion as potentially infected patients. Nine-hundred to 1,000 of these admissions were randomly selected from each health system for chart review, and a proportional number of patients who did not meet chart review eligibility criteria were also included and deemed not infected.InterventionsNone.Measurements and main resultsThe accuracy of published billing code criteria by Angus et al and electronic health record criteria by Rhee et al and Seymour et al (Sepsis-3) was determined using the manual chart review results as the gold standard. A total of 5,215 patients were included, with 2,874 encounters analyzed via chart review and a proportional 2,341 added who did not meet chart review eligibility criteria. In the study cohort, 27.5% of admissions had at least one infection. This was most similar to the percentage of admissions with blood culture orders (26.8%), Angus infection criteria (28.7%), and the Sepsis-3 criteria (30.4%). Sepsis-3 criteria was the most sensitive (81%), followed by Angus (77%) and Rhee (52%), while Rhee (97%) and Angus (90%) were more specific than the Sepsis-3 criteria (89%). Results were similar for patients with organ dysfunction during their admission.ConclusionsPublished criteria have a wide range of accuracy for identifying infected patients, with the Sepsis-3 criteria being the most sensitive and Rhee criteria being the most specific. These findings have important implications for studies investigating the burden of sepsis on a local and national level.

Project description:The US federal government is spending billions of dollars in physician incentives to encourage the meaningful use of electronic health records (EHRs). Although the use of EHRs has potential to improve patient health outcomes, the existing evidence has been limited and inconsistent.To examine the association between implementing a commercially available outpatient EHR and emergency department (ED) visits, hospitalizations, and office visits for patients with diabetes mellitus.Staggered EHR implementation across outpatient clinics in an integrated delivery system (Kaiser Permanente Northern California) between 2005 and 2008 created an opportunity for studying changes associated with EHR use. Among a population-based sample of 169,711 patients with diabetes between 2004 and 2009, we analyzed 4,997,585 person-months before EHR implementation and 4,648,572 person-months after an EHR was being used by patients' physicians.We examined the association between EHR use and unfavorable clinical events (ED visits and hospitalizations) and office visit use among patients with diabetes, using multivariable regression with patient-level fixed-effect analyses and adjustment for trends over time.In multivariable analyses, use of the EHR was associated with a statistically significantly decreased number of ED visits, 28.80 fewer visits per 1000 patients annually (95% CI, 20.28 to 37.32), from a mean of 519.12 visits per 1000 patients annually without using the EHR to 490.32 per 1000 patients when using the EHR. The EHR was also associated with 13.10 fewer hospitalizations per 1000 patients annually (95% CI, 7.37 to 18.82), from a mean of 251.60 hospitalizations per 1000 patients annually with no EHR to 238.50 per 1000 patients annually when using the EHR. There were similar statistically significant reductions in nonelective hospitalizations (10.92 fewer per 1000 patients annually) and hospitalizations for ambulatory care-sensitive conditions (7.08 fewer per 1000 patients annually). There was no statistically significant association between EHR use and office visit rates.Among patients with diabetes, use of an outpatient EHR in an integrated delivery system was associated with modest reductions in ED visits and hospitalizations but not office visit rates. Further studies are needed to quantify the association of EHR use with changes in costs.

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The effect of number of healthcare visits on study sample selection in electronic health record data.

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The effect of number of healthcare visits on study sample selection in electronic health record data.

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