Evidence map›Paper›PMID 31209679›Full record

ArticleHEC forum : an interdisciplinary journal on hospitals' ethical and legal issues2019

Transformation of the Doctor-Patient Relationship: Big Data, Accountable Care, and Predictive Health Analytics.

Seuli Bose Brill, Karen O Moss, Laura Prater

Abstract read
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In one paragraph

Article in HEC forum : an interdisciplinary journal on hospitals' ethical and legal issues, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 3 pooled it
–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, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Review
  6. Article
  7. Review
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.

Seuli Bose BrillThe Division of General Internal Medicine at The Ohio State University Wexner Medical Center, 895 Yard Street, Columbus, OH, 43212, USA. Seuli.Brill@osumc.edu.ORCID http://orcid.org/0000-0003-4290-8992
Karen O MossThe Ohio State University College of Nursing, 368 Newton Hall, 1585 Neil Avenue, Columbus, OH, 43210, USA.
Laura PraterThe Division of General Internal Medicine at The Ohio State University Wexner Medical Center, 895 Yard Street, Columbus, OH, 43212, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The medical profession is steeped in traditions that guide its practice. These traditions were developed to preserve the well-being of patients. Transformations in science, technology, and society, while maintaining a self-governance structure that drives the goal of care provision, have remained hallmarks of the profession. The purpose of this paper is to examine ethical challenges in health care as it relates to Big Data, Accountable Care Organizations, and Health Care Predictive Analytics using the principles of biomedical ethics laid out by Beauchamp and Childress (autonomy, beneficence, non-maleficence, and justice). Among these are the use of Electronic Health Records within stipulations of the Health Insurance Portability and Accountability Act. Clinicians are well-positioned to impact health policy development to address ethical issues associated with the use of Big Data, Accountable Care, and Health Care Predictive Analytics as we work to transform the doctor-patient relationship towards improving population health outcomes and creating a healthier society.

Indexed as

Big DataPhysician-Patient RelationsAccountable Care OrganizationsData ScienceHumansAccountable Care OrganizationsBig DataDoctor–patient relationshipHealth analyticsHealth care ethics

Identifiers

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.