MétaCan
Menu
Back to cohort
Record W3164381515 · doi:10.1016/j.xkme.2021.03.009

Integrated Digital Health System Tools to Support Decision Making and Treatment Preparation in CKD: The PREPARE NOW Study

2021· article· en· W3164381515 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKidney Medicine · 2021
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of ManitobaSeven Oaks General Hospital
FundersPatient-Centered Outcomes Research Institute
KeywordsMedicineKidney diseaseNephrologyClinical decision support systemRenal replacement therapyDecision support systemElectronic health recordHealth careFamily medicineIntensive care medicineMedical emergencyNursingInternal medicineComputer science

Abstract

fetched live from OpenAlex

RATIONALE & OBJECTIVE: Digital health system tools to support shared decision making and preparation for kidney replacement treatments for patients with chronic kidney disease (CKD) are needed. STUDY DESIGN: Descriptive study of the implementation of digital infrastructure to support a patient-centered health system intervention. SETTING & PARTICIPANTS: 4 CKD clinics within a large integrated health system. EXPOSURE: We developed an integrated suite of digital engagement tools to support patients' shared decision making and preparation for kidney failure treatments. Tools included an automated CKD patient registry and risk prediction algorithm within the electronic health record (EHR) to identify and prioritize patients in need of nurse case management to facilitate shared decision making and preparation for kidney replacement treatments, an electronic patient-facing values clarification tool, a tracking application to document patients' preparation for treatments, and an EHR work flow to broadcast patients' treatment preferences to all health care providers. OUTCOMES: Uptake and acceptability. ANALYTIC APPROACH: Mixed methods. RESULTS: From July 1, 2017, through June 30, 2018, the CKD registry identified 1,032 patients in 4 nephrology clinics, of whom 243 (24%) were identified as high risk for progressing to kidney failure within 2 years. Kidney Transitions Specialists enrolled 117 (48%) high-risk patients by the end of year 1. The values tool was completed by 30/33 (91%) patients who attended kidney modality education. Nurse case managers used the tracking application for 100% of patients to document 287 planning steps for kidney replacement therapy. Most (87%) high-risk patients had their preferred kidney replacement modality documented and displayed in the EHR. Nurse case managers reported that the tools facilitated their identification of patients needing support and their navigation activities. LIMITATIONS: Single institution, short duration. CONCLUSIONS: Digital health system tools facilitated rapid identification of patients needing shared and informed decision making and their preparation for kidney replacement treatments. FUNDING: This work was supported through a Patient-Centered Outcomes Research Institute (PCORI) Project Program Award (IHS-1409-20967). TRIAL REGISTRATION: ClinicalTrials.gov NCT02722382.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.089
GPT teacher head0.474
Teacher spread0.385 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it