Evidence map›Paper›PMID 41413263›Full record

ReviewNPJ digital medicine2025

Meeting the challenges of electronic health record (EHR) optimization.

Sonya Makhni, Paul Cerrato, Jose Rico, Shehzad Niazi, Jack O'Horo, Steve Peters, Vijay Shah, John Halamka

Abstract readReview
In one paragraph

Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sonya MakhniMayo Clinic Platform, Rochester, MN, USA. makhni.sonya@mayo.edu.
Paul CerratoMayo Clinic Platform, Rochester, MN, USA.
Jose RicoMayo Clinic Platform, Rochester, MN, USA.
Shehzad NiaziMayo Clinic Florida, Jacksonville, FL, USA.
Jack O'HoroMayo Clinic, Rochester, MN, USA.
Steve PetersMayo Clinic, Rochester, MN, USA.
Vijay ShahMayo Clinic, Rochester, MN, USA.
John HalamkaMayo Clinic Platform, Rochester, MN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic health records (EHRs) offer many potential benefits to clinicians in information accessibility, e-prescriptions, care coordination and more. Despite these benefits, most clinicians experience numerous shortcomings with EHRs that impact user satisfaction, efficiency, patient-physician relationships, and patient safety. With the widespread adoption of this technology already achieved, we must understand the shortcomings of EHRs to ensure these systems fulfill their intended purpose of improving patient care and clinical efficiency. Many systems inadequately incorporate user-friendly best practices and flexible architectures, limiting user control and customization. This review elaborates on usability, process, technology, and policy challenges. We also describe future solutions to mitigate these shortcomings and improve EHR functionality in clinical settings. Effective EHR improvement requires addressing known challenges through improved processes and technological innovation. Future EHR systems must be dynamic and adaptive, allowing seamless implementation of AI and related technologies across diverse clinical contexts and augmenting instead of impeding clinicians.

Identifiers

PMID41413263
PMCPMC12769529

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.