Evidence map›Paper›PMID 31798215›Full record

ArticleJurimetrics2018

GRANULAR PATIENT CONTROL OF PERSONAL HEALTH INFORMATION: FEDERAL AND STATE LAW CONSIDERATIONS.

Michael J Saks, Adela Grando, Anita Murcko, Chase Millea

Abstract read
In one paragraph

Article in Jurimetrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. FHIR Granular Sensitive Data Segmentation.Applied clinical informatics · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Pilot evaluation of sensitive data segmentation technology for privacy.International journal of medical informatics · 2020
    Article
  9. Article
  10. Article
  11. 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

4 authors.

Michael J SaksRegents Professor in the Sandra Day O'Connor College of Law and Department of Psychology at Arizona State University, 111 E Taylor Street, Phoenix, AZ 85004, michael.saks@asu.edu.
Adela GrandoAssistant Professor in the Department of Biomedical Informatics at Arizona State University, 13212 E. Shea Boulevard, Scottsdale, AZ 85259, agrando@asu.edu.
Anita MurckoClinical Associate Professor in the Department of Biomedical Informatics at Arizona State University, anita.murcko@asu.edu.
Chase MilleaHealth Policy Attorney, serving as the privacy & compliance officer at Health Current in Arizona; chase.millea@healthcurrent.org.

Funding

My Data Choices, evaluation of effective consent strategies for patients with behavioral health conditionsR01MH108992 · NIMH · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI GRANDO, MARIA ADELA · 2016 to 2020
$2.0M
NIMH NIH HHS R01 MH108992
6 · The paper itself

Abstract

The advent of electronic medical records and health information exchanges has facilitated the possibility of patients exercising increasingly granular control over sensitive health information. In principle, patients should be able to control which of their health information is made accessible to which of their healthcare providers. To meet this goal, the architects of any system of granular control of patients' health information face a variety of challenges. In addition to technical, ethical, and prudential considerations, the architects of any effective system must also ensure compliance with applicable legal requirements. The extent of a patient's permissible control depends upon whether governing law

Identifiers

PMID31798215
PMCPMC6890413

What OpenQuestion holds

Textmetadata
LicenceTDM
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.