Dataset Information

Adverse inpatient outcomes during the transition to a new electronic health record system: observational study.

ABSTRACT: To assess the short term association of inpatient implementation of electronic health records (EHRs) with patient outcomes of mortality, readmissions, and adverse safety events. Observational study with difference-in-differences analysis. Medicare, 2011-12. Patients admitted to 17 study hospitals with a verifiable "go live" date for implementation of inpatient EHRs during 2011-12, and 399 control hospitals in the same hospital referral region. All cause readmission within 30 days of discharge, all cause mortality within 30 days of admission, and adverse safety events as defined by the patient safety for selected indicators (PSI)-90 composite measure among Medicare beneficiaries admitted to one of these hospitals 90 days before and 90 days after implementation of the EHRs (n=28?235 and 26?453 admissions), compared with the control group of all contemporaneous admissions to hospitals in the same hospital referral region (n=284?632 and 276?513 admissions). Analyses were adjusted for beneficiaries' sociodemographic and clinical characteristics. Before and after implementation, characteristics of admissions were similar in both study and control hospitals. Among study hospitals, unadjusted 30 day mortality (6.74% to 7.15%, P=0.06) and adverse safety event rates (10.5 to 11.4 events per 1000 admissions, P=0.34) did not significantly change after implementation of EHRs. There was an unadjusted decrease in 30 day readmission rates, from 19.9% to 19.0% post-implementation (P=0.02). In difference-in-differences analysis, however, there was no significant change in any outcome between pre-implementation and post-implementation periods (all P?0.13). Despite concerns that implementation of EHRs might adversely impact patient care during the acute transition period, we found no overall negative association of such implementation on short term inpatient mortality, adverse safety events, or readmissions in the Medicare population across 17 US hospitals.

SUBMITTER: Barnett ML

PROVIDER: S-EPMC4964115 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Adverse inpatient outcomes during the transition to a new electronic health record system: observational study.

Barnett Michael L ML Mehrotra Ateev A Jena Anupam B AB

BMJ (Clinical research ed.) 20160728

<h4>Objective</h4> To assess the short term association of inpatient implementation of electronic health records (EHRs) with patient outcomes of mortality, readmissions, and adverse safety events.<h4>Design</h4> Observational study with difference-in-differences analysis.<h4>Setting</h4> Medicare, 2011-12.<h4>Participants</h4> Patients admitted to 17 study hospitals with a verifiable "go live" date for implementation of inpatient EHRs during 2011-12, and 399 control hospitals in the same hospita ...[more]

PMID: 27471242

Similar Datasets

Project description:BackgroundEHR systems are widely used in hospitals and primary care centres but it is usually difficult to share information and to collect patient data for clinical research. This is partly due to the different proprietary information models and inconsistent data quality. Our objective was to provide a more flexible solution enabling the clinicians to define which data to be recorded and shared for both routine documentation and clinical studies. The data should be possible to reuse through a common set of variable definitions providing a consistent nomenclature and validation of data. Another objective was that the templates used for the data entry and presentation should be possible to use in combination with the existing EHR systems.MethodsWe have designed and developed a template based system (called Julius) that was integrated with existing EHR systems. The system is driven by the medical domain knowledge defined by clinicians in the form of templates and variable definitions stored in a common data repository. The system architecture consists of three layers. The presentation layer is purely web-based, which facilitates integration with existing EHR products. The domain layer consists of the template design system, a variable/clinical concept definition system, the transformation and validation logic all implemented in Java. The data source layer utilizes an object relational mapping tool and a relational database.ResultsThe Julius system has been implemented, tested and deployed to three health care units in Stockholm, Sweden. The initial responses from the pilot users were positive. The template system facilitates patient data collection in many ways. The experience of using the template system suggests that enabling the clinicians to be in control of the system, is a good way to add supplementary functionality to the present EHR systems.ConclusionThe approach of the template system in combination with various local EHR systems can facilitate the sharing and reuse of validated clinical information from different health care units. However, future system developments for these purposes should consider using the openEHR/CEN models with shareable archetypes.

Project description:BACKGROUND:The secondary use of electronic health records (EHRs) promises to facilitate medical research. We reviewed general data requirements in observational studies and analyzed the feasibility of conducting observational studies with structured EHR data, in particular diagnosis and procedure codes. METHODS:After reviewing published observational studies from the University Hospital of Erlangen for general data requirements, we identified three different study populations for the feasibility analysis with eligibility criteria from three exemplary observational studies. For each study population, we evaluated the availability of relevant patient characteristics in our EHR, including outcome and exposure variables. To assess data quality, we computed distributions of relevant patient characteristics from the available structured EHR data and compared them to those of the original studies. We implemented computed phenotypes for patient characteristics where necessary. In random samples, we evaluated how well structured patient characteristics agreed with a gold standard from manually interpreted free texts. We categorized our findings using the four data quality dimensions "completeness", "correctness", "currency" and "granularity". RESULTS:Reviewing general data requirements, we found that some investigators supplement routine data with questionnaires, interviews and follow-up examinations. We included 847 subjects in the feasibility analysis (Study 1 n?=?411, Study 2 n?=?423, Study 3 n?=?13). All eligibility criteria from two studies were available in structured data, while one study required computed phenotypes in eligibility criteria. In one study, we found that all necessary patient characteristics were documented at least once in either structured or unstructured data. In another study, all exposure and outcome variables were available in structured data, while in the other one unstructured data had to be consulted. The comparison of patient characteristics distributions, as computed from structured data, with those from the original study yielded similar distributions as well as indications of underreporting. We observed violations in all four data quality dimensions. CONCLUSIONS:While we found relevant patient characteristics available in structured EHR data, data quality problems may entail that it remains a case-by-case decision whether diagnosis and procedure codes are sufficient to underpin observational studies. Free-text data or subsequently supplementary study data may be important to complement a comprehensive patient history.

Dataset Information

Adverse inpatient outcomes during the transition to a new electronic health record system: observational study.

Publications

Adverse inpatient outcomes during the transition to a new electronic health record system: observational study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets