Digital Outpatient Services for Adults: Development of an Intervention and Protocol for a Multicenter Non–Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Health care services are being challenged by an increasing number of patients and limited resources. Hence, research investigating options to reduce costs and increase effectiveness is warranted. Digital outpatient services can provide flexible and tailored follow-up, improve patients' health literacy, and facilitate the identification of adverse courses of disease. However, previous research largely focused on disease-specific contexts and outcomes. Therefore, research on digital services investigating generic outcomes such as health literacy is warranted. OBJECTIVE: This article aims to describe the "digital outpatient service" intervention and present the protocol for an ongoing multicenter, nonrandomized trial evaluating this intervention. METHODS: Based on previous experiences and evidence-based knowledge, we developed this intervention through patient-journey maps in collaboration with each clinical specialty. The patients gain access to a mobile app for self-monitoring and patient-reported outcomes and a chat for contact between the patients and health care workers. The health care workers' dashboard includes a traffic light system to draw attention to the most urgent patient reports. In this multicenter, non-randomized controlled trial, patients are allocated to the control group receiving standard care or the 6-month intervention. Eligible patients are aged 18 years or older who receive outpatient care at the neurology, lung, pain, or cancer departments at 2 university hospitals in Norway. Our evaluation will include patient-reported outcomes, qualitative interviews, and clinical measures. The primary outcome will be health literacy using the Health Literacy Questionnaire. A sample size of 165 participants is split into a 1:2 ratio in favor of the intervention. We will analyze quantitative data in SPSS (IBM Corp) using descriptive statistics and logistic regression, and qualitative data using thematic analysis. RESULTS: This trial started in September 2021, and the intervention started in January 2022. Recruitment has ended, with 55 patients in the control group and 107 patients in the intervention group. Follow-up is expected to end in July 2023, with results expected to be obtained in December 2023. CONCLUSIONS: This study will evaluate an intervention facilitated by an already certified digital multicomponent solution, with intervention content based on patient-reported outcomes, health literacy, and self-monitoring. The intervention is specifically tailored to each participating center and the needs of their patients using patient journey maps. The comprehensive and generic evaluation of this digital outpatient service intervention is a strength as it targets a heterogeneous sample of patients. Thus, this study will provide important knowledge about the applicability and effects of digital health care services. As a result, patients and health care workers will gain a new, evidence-based understanding of whether and how digital tools may be used in clinical care. TRIAL REGISTRATION: ClinicalTrials.gov NCT05068869; https://clinicaltrials.gov/ct2/show/NCT05068869. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46649.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.074 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".