Evidence map›Paper›PMID 35467730›Full record

ArticleJAMA network open2022

Patient and Clinician Perceptions of Factors Relevant to Ideal Specialty Consultations.

Stephanie D Roche, Anna C Johansson, Jaclyn Giannakoulis, Michael N Cocchi, Michael D Howell, Bruce Landon, Jennifer P Stevens

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.7field-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

3 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. 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 at 3 institutions in 1 country.

Stephanie D RocheDepartment of Health Care Quality, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Anna C JohanssonCenter for Health Care Delivery Science, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Jaclyn GiannakoulisPatient and Family Advisory Council, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Michael N CocchiDepartment of Health Care Quality, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Michael D HowellGoogle Health, Palo Alto, California.
Bruce LandonCenter for Health Care Delivery Science, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Jennifer P StevensCenter for Health Care Delivery Science, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Beth Israel Deaconess Medical Center · USGoogle (United States) · USHarvard University · US

Funding

A multistakeholder examination of the drivers and value of inpatient consultationK08HS024288 · AHRQ · BETH ISRAEL DEACONESS MEDICAL CENTER · PI STEVENS, JENNIFER P · 2016 to 2020
$752k
AHRQ HHS K08 HS024288
6 · The paper itself

Abstract

Importance: Inpatient subspecialty consultations, a common and expensive practice within inpatient medicine, do not always go well; however, little is known about the failure modes of consultation, thus making it difficult to identify interventions to improve consultation quality. Objective: To understand how stakeholders envision the ideal inpatient consultation and identify how and why consultations commonly fall short of this ideal. Design, Setting, and Participants: This qualitative study used in-depth, semistructured interviews collected from April to October 2017 and analyzed from January 2018 to February 2020 using conventional content analysis. The setting was a single academic medical center in Boston, Massachusetts. Participants were hospitalists and specialists who had requested or performed a consultation for a non-intensive care unit patient in the previous 4 months, patients who had received a consultation while hospitalized at the medical center in the previous 15 months, and family members of such patients. Main Outcomes and Measures: Consultation experiences reported by participants. Clinicians were asked about characteristics of the ideal consultation, positive and negative consultation experiences, costs and benefits, and suggested improvements. Patients and family members were asked about their consultation experience, changes in care, communication preferences, and suggested improvements. Results: The study included 38 participants: 17 specialists, 13 hospitalists, 4 patients, and 4 family members. More than half (21 of 38) of the participants were female. There were 11 key information exchanges identified that occur among the specialist team, primary team, and patient/family during an ideal consultation. These exchanges are time sensitive and primarily carried out through unwritten protocols. We also identified 6 defects (process failures) that commonly derail information exchanges (complete omission, exclusion of a key stakeholder, poor timing, incomplete or inaccurate information, and misinterpretation) and 5 contextual factors (roles and boundaries, professionalism, team hierarchy, availability, and operational know-how) that influence how information exchange unfolds, making some consultations more prone to defects. Conclusions and Relevance: Successful inpatient consultation requires a complicated, sequenced series of time-sensitive information exchanges that are highly vulnerable to failure. Maximizing the benefit of consultations will likely entail not only minimizing low-value consultations but also actively preventing defects, such as information inaccuracies and misinterpretation, that commonly derail the consultation process.

Indexed as

MedicineReferral and ConsultationCommunicationFamilyFemaleHumansMaleSpecialization

Identifiers

PMID35467730
PMCPMC9039767
OpenAlexW4224640200

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.