Normalizing Telemonitoring in Nurse-Led Care Models for Complex Chronic Patient Populations: Case Study
Bibliographic record
Abstract
BACKGROUND: The implementation of telemonitoring (TM) has been successful in terms of the overall feasibility and adoption in single disease care models. However, a lack of available research focused on nurse-led implementations of TM that targets patients with multiple and complex chronic conditions (CCC) hinders the scale and spread to these patient populations. In particular, little is known about the clinical perspective on the implementation of TM for patients with CCC in outpatient care. OBJECTIVE: This study aims to better understand the perspective of the clinical team (both frontline clinicians and those in administrative positions) on the implementation and normalization of TM for complex patients in a nurse-led clinic model. METHODS: A pragmatic, 6-month implementation study was conducted to embed multicondition TM, including heart failure, hypertension, and diabetes, into an integrated nurse-led model of care. Throughout the study, clinical team members were observed, and a chart review was conducted of the care provided during this time. At the end of the study, clinical team members participated in qualitative interviews and completed the adapted Normalization Measure Development questionnaires. The Normalization Process Theory guided the deductive data analysis. RESULTS: Overall, 9 team members participated in the study as part of a larger feasibility study of the TM program, of which 26 patients were enrolled. Team members had a shared understanding of the purpose and value of TM as an intervention embedded within their practice to meet the diverse needs of their patients with CCC. TM aligned well with existing chronic care practices in several ways, yet it changed the process of care delivery (ie, interactional workability subconstruct). Effective TM normalization in nurse-led care requires rethinking of clinical workflows to incorporate TM, relationship development between the clinicians and their patients, communication with the interdisciplinary team, and frequent clinical care oversight. This was captured well through the subconstructs of skill set workability, relational integration, and contextual integration of the Normalization Process Theory. CONCLUSIONS: Clinicians successfully adopted TM into their everyday practice such that some providers felt their role would be significantly and negatively affected without TM. This study demonstrated that smartphone-based TM systems complemented the routine and challenging clinical work caring for patients with CCC in an integrated nurse-led care model.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".