Evidence map›Paper›PMID 37027184›Full record

ArticleJMIR formative research2023

Digital Health at Enterprise Scale: Evaluation Framework for Selecting Patient-Facing Software in a Digital-First Health System.

Martin Shapiro, Sondra Renly, Ali Maiorano, Jerry Young, Eli Medina, Aaron Neinstein, Anobel Y Odisho

Abstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. 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

7 authors.

Martin ShapiroDepartment of Family and Community Medicine, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0001-7394-4748
Sondra RenlyCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-2436-0305
Ali MaioranoCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-4697-6186
Jerry YoungCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0003-0836-1479
Eli MedinaCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0001-6189-7235
Aaron NeinsteinCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-9774-7180
Anobel Y OdishoCenter for Digital Health Innovation, University of California, San Francisco, San Francisco, CA, United States.ORCID https://orcid.org/0000-0003-0975-0812

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The digital transformation of our health care system will require not only digitization of existing tools but also a redesign of our care delivery system and collaboration with digital partners. Traditional patient journeys are reactive to symptom presentation and delayed by health care system-centric scheduling, leading to poor experience and avoidable adverse outcomes. Patient journeys will be reimagined to a digital health pathway that seamlessly integrates various care experiences from telemedicine, remote monitoring, to in-person clinic visits. Through centering the care delivery around the patients, they can have more delightful experiences and enjoy the quality of standardized condition pathways and outcomes. To design and implement digital health pathways at scale, enterprise health care systems need to develop capabilities and partnerships in human-centered design, operational workflow, clinical content management, communication channels and mechanisms, reporting and analytics, standards-based integration, security and data management, and scalability. Using a human-centered design methodology, care pathways will be built upon an understanding of the unmet needs of the patients to have a more enjoyable experience of care with improved clinical outcomes. To power this digital care pathway, enterprises will choose to build or partner for clinical content management to operationalize up-to-date, best-in-class pathways. With this clinical engine, this digital solution will engage with patients through multimodal communication modalities, including written, audio, photo, or video, throughout the patient journey. Leadership teams will review reporting and analytics functions to track that the digital care pathways will be iterated to improve patient experience, clinical metrics, and operational efficiency. On the backend, standards-based integration will allow this system to be built in conjunction with the electronic medical record and other data systems to provide safe and efficient use of the digital care solution. For protecting patient information and compliance, a security and data management strategy is critical to derisking breeches and preserving privacy. Finally, a framework of technical scalability will allow digital care pathways to proliferate throughout the enterprise and support the entire patient population. This framework empowers enterprise health care systems to avoid collecting a fragmented series of one-off solutions but develop a sustainable concerted roadmap to the future of proactive intelligent patient care.

Indexed as

artificial intelligencedigital health pathwayeHealthenterprise digital healthevaluation frameworkframeworkhealthcare deliveryhealthcare systemintelligent careintelligent systemprivacysecurityservice deliverysystems designtelehealthtelemedicine

Identifiers

PMID37027184
PMCPMC10131984

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.