Evidence map›Paper›PMID 31245559›Full record

ArticleLearning health systems2017

Knowledge management in the era of digital medicine: A programmatic approach to optimize patient care in an academic medical center.

Jane L Shellum, Rick A Nishimura, Dawn S Milliner, Charles M Harper, John H Noseworthy

Open access · goldAbstract read
In one paragraph

Article in Learning health systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 11% of its field
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 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. An Examination of the Statistical Laws of Semantic Change in Clinical Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
    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

5 authors at 3 institutions in 1 country.

Jane L ShellumInformation Technology, Knowledge and Delivery Center Mayo Clinic Rochester Minnesota.
Rick A NishimuraMayo Clinic College of Medicine Mayo Clinic Rochester Minnesota.
Dawn S MillinerMayo Clinic College of Medicine Mayo Clinic Rochester Minnesota.
Charles M HarperMayo Clinic College of Medicine Mayo Clinic Rochester Minnesota.
John H NoseworthyMayo Clinic College of Medicine Mayo Clinic Rochester Minnesota.
Mayo Clinic · USMayo Clinic in Arizona · USMayo Clinic in Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe pace of medical discovery is accelerating to the point where caregivers can no longer keep up with the latest diagnosis or treatment recommendations. At the same time, sophisticated and complex electronic medical records and clinical systems are generating increasing volumes of patient data, making it difficult to find the important information required for patient care. To address these challenges, Mayo Clinic established a knowledge management program to curate, store, and disseminate clinical knowledge.

methodsThe authors describe AskMayoExpert, a point-of-care knowledge delivery system, and discuss the process by which the clinical knowledge is captured, vetted by clinicians, annotated, and stored in a knowledge content management system. The content generated for AskMayoExpert is considered to be core clinical content and serves as the basis for knowledge diffusion to clinicians through order sets and clinical decision support rules, as well as to patients and consumers through patient education materials and internet content. The authors evaluate alternative approaches for better integration of knowledge into the clinical workflow through development of computer-interpretable care process models.

resultsEach of the modeling approaches evaluated has shown promise. However, because each of them addresses the problem from a different perspective, there have been challenges in coming to a common model. Given the current state of guideline modeling and the need for a near-term solution, Mayo Clinic will likely focus on breaking down care process models into components and on standardization of those components, deferring, for now, the orchestration.

conclusionA point-of-care knowledge resource developed to support an individualized approach to patient care has grown into a formal knowledge management program. Translation of the textual knowledge into machine executable knowledge will allow integration of the knowledge with specific patient data and truly serve as a colleague and mentor for the physicians taking care of the patient.

Indexed as

computer‐interpretable guidelinesknowledge managementknowledge representation

Identifiers

PMID31245559
PMCPMC6508510
OpenAlexW2587795588

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.