Embedding a State-Wide Prescription Drug Monitoring Program into the Electronic Health Record.

Chad M Hodge, James Hellewell, Ian Ford, Kellyann Nielsen, Philip Brewster, Jeffrey Henrie

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. 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

6 authors.

Chad M HodgeIntermountain Healthcare, Clinical Informatics, Salt Lake City, UT, USA.

James HellewellIntermountain Healthcare, Clinical Informatics, Salt Lake City, UT, USA.

Ian FordIntermountain Healthcare, Clinical Informatics, Salt Lake City, UT, USA.

Kellyann NielsenIntermountain Healthcare, Clinical Informatics, Salt Lake City, UT, USA.

Philip BrewsterUtah Division of Occupational and Professional Licensing, Utah Department of Commerce, Salt Lake City, UT, USA.

Jeffrey HenrieUtah Division of Occupational and Professional Licensing, Utah Department of Commerce, Salt Lake City, UT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A new FHIR-based (fast healthcare interoperability resource), EHR-integrated (electronic health record) application was created that embeds directly into prescribers' workflows. The intervention automatically performs MME (morphine milligram equivalent) calculations, highlights unsafe thresholds, while also presenting controlled-substance medications each patient is using. Using this intervention, the number of clinicians who have checked a patient's controlled substance prescription data has increased 57.4%. The number of patients being checked increased by 9.2%. The number of opioid prescriptions written after checking the new interventions data increased from 9% to 14%. Integrating these data into the EHR has saved over 1600 hours of labor per year. This work has also led to accruing five bonus points from the Medicare promoting interoperability attestation program. This initiative, and others like it, have helped Intermountain Healthcare decrease controlled substance pills by nearly 11 million, in the past five years.

Indexed as

Prescription Drug Monitoring ProgramsAgedAnalgesics, OpioidControlled SubstancesDrug PrescriptionsElectronic Health RecordsHumansMedicarePractice Patterns, Physicians'United StatesAnalgesics, OpioidControlled Substances

Identifiers

PMID37128370

PMCPMC10148357

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.