Evidence map›Paper›PMID 27471242›Full record

Observational studyBMJ (Clinical research ed.)2016

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

Michael L Barnett, Ateev Mehrotra, Anupam B Jena

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in BMJ (Clinical research ed.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Electronic Health Record Transitions-How to Make Them Work.Journal of general internal medicine · 2023
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  18. Changes in hospital bond ratings after the transition to a new electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2018
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Michael L BarnettDepartment of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Ateev MehrotraDepartment of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Anupam B JenaDepartment of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA Department of Medicine, Massachusetts General Hospital, Boston, MA, USA jena@hcp.med.harvard.edu.

Funding

Physician Determinants of Health Care Spending, Quality, and Patient OutcomesDP5OD017897 · OD · HARVARD MEDICAL SCHOOL · PI JENA, ANUPAM BAPU · 2013 to 2017
$2.1M
NIH HHS DP5 OD017897
6 · The paper itself

Abstract

objectiveTo assess the short term association of inpatient implementation of electronic health records (EHRs) with patient outcomes of mortality, readmissions, and adverse safety events.

designObservational study with difference-in-differences analysis.

settingMedicare, 2011-12.

participantsPatients 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.

main outcome measuresAll 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.

resultsBefore 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).

conclusionsDespite 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.

Indexed as

AgedAged, 80 and overElectronic Health RecordsFemaleHealth Plan ImplementationHumansInpatientsMaleMedicarePatient DischargePatient ReadmissionPatient TransferUnited States

Identifiers

PMID27471242
PMCPMC4964115

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.