Evidence map›Paper›PMID 32308841›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019

Exploring Different Approaches in Measuring EHR-based Adherence to Best Practice - A Case Study with Order Sets and Associated Outcomes.

Nathan C Hulse, Jaehoon Lee, José Benuzillo

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Nathan C HulseIntermountain Healthcare, Salt Lake City, UT.
Jaehoon LeeIntermountain Healthcare, Salt Lake City, UT.
José BenuzilloIntermountain Healthcare, Salt Lake City, UT.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In connection with a recent enterprise-wide rollout of a new electronic health record, Intermountain Healthcare is investing significant effort in building a central library of best-practice order sets. These order sets represent best practice guidelines for specific clinical scenarios and are deployed with the intent of standardizing care, reducing variation, and consistently delivering good clinical outcomes to the populations we serve. The importance of measuring their use and the level to which caregivers adhere to these standards becomes an important factor in understanding and characterizing the impact that they deliver. Notwithstanding the importance of these metrics, well- defined methods for measuring adherence to a given clinical guideline as delivered through an order set are not fully characterized in the medical literature. In this paper, we describe initial efforts at measuring compliance to a defined 'best practice' standard by means of content utilization analysis, a calculated adherence model, and relevant clinical key performance indicators. The degree to which specified clinical outcomes vary across these measurement models are compared for a group of order sets tied to treating coronary artery bypass graft patients and heart failure patients. While the patterns derived from this analysis show some uncertainty, more granular methods that look at line-item, or 'order level' detail reveal more significant differences in the corresponding set of outcomes than higher-level adherence surrogates.

Indexed as

Electronic Health RecordsGuideline AdherenceMedical Order Entry SystemsPractice Guidelines as TopicCoronary Artery BypassDelivery of Health Care, IntegratedHeart FailureHumansLength of StayMyocardial InfarctionUtah

Identifiers

PMID32308841
PMCPMC7153084

What OpenQuestion holds

Textmetadata
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.