Integrating Telehealth Care-Generated Data With the Family Practice Electronic Medical Record: Qualitative Exploration of the Views of Primary Care Staff
Bibliographic record
Abstract
BACKGROUND: Telehealth care is increasingly being employed in the management of long-term illness. Current systems are largely managed via "stand-alone" websites, which require additional log-ons for clinicians to view their patients' symptom records and physiological measurements leading to frustrating delays and sometimes failure to engage with the record. However, there are challenges to the full integration of patient-acquired data into family physicians' electronic medical records (EMR) in terms of reliability, how such data can best be summarized and presented to avoid overload to the clinicians, and how clarity of responsibility is managed when multiple agencies are involved. OBJECTIVE: We aimed to explore the views of primary care clinicians on the acceptability, clinical utility, and, in particular, the benefits and risks of integrating patient-generated telehealth care data into the family practice EMR and to explore how these data should be summarized and presented in order to facilitate use in routine care. METHODS: In our qualitative study, we carried out semi-structured interviews with clinicians with experience of and naïve to telehealth care following demonstration of pilot software, which illustrated various methods by which data could be incorporated into the EMR. RESULTS: We interviewed 20 clinicians and found 2 overarching themes of "workload" and "safety". Although clinicians were largely positive about integrating telehealth care data into the EMR, they were concerned about the potential increased workload and safety issues, particularly in respect to error due to data overload. They suggested these issues could be mitigated by good system design that summarized and presented data such that they facilitated seamless integration with clinicians' current routine processes for managing data flows, and ensured clear lines of communication and responsibility between multiple professionals involved in patients' care. CONCLUSIONS: Family physicians and their teams are likely to be receptive to and see the benefits of integrating telehealth-generated data into the EMR. Our study identified some of the key challenges that must be overcome to facilitate integration of telehealth care data. This work particularly underlines the importance of actively engaging with clinicians to ensure that systems are designed that align well with existing practice data-flow management systems and facilitate safe multiprofessional patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.012 |
| 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; both teacher heads agree on what is shown here.
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".