“The Doctor Needs to Know”: Acceptability of Smartphone Location Tracking for Care Coordination
Bibliographic record
Abstract
BACKGROUND: Care coordination can be highly challenging to carry out. When care is fragmented across health systems and providers, there is an increased likelihood of hospital readmissions and wasteful health care spending. During and after care transitions, smartphones have the potential to bolster information transfer and care coordination. However, little research has examined patients' perceptions of using smartphones to coordinate care. OBJECTIVE: This study's primary objective was to explore patient acceptability of a smartphone app that could facilitate care coordination in a safety net setting. Our secondary objective was to identify how clinicians and other members of primary care teams could use this app to coordinate care. METHODS: This qualitative study was conducted at a federally qualified health center in metropolitan Chicago, IL. We conducted four focus groups (two in English, two in Spanish) with high-risk adults who owned a smartphone and received services from an organizational care management program. We also conducted structured interviews with clinicians and a group interview with care managers. Focus groups elicited patients' perceptions of a smartphone app designed to: (1) identify emergency department (ED) visits and inpatient stays using real-time location data; (2) send automated notifications (ie, alerts) to users' phones, asking whether they were a patient in the hospital; and (3) send automated messages to primary care teams to notify them about patients' confirmed ED visits and inpatient stays. Focus group transcripts were coded based on emergent themes. Clinicians and care managers were asked about messages they would like to receive from the app. RESULTS: Five main themes emerged in patient focus group discussions. First, participants expressed a high degree of willingness to use the proposed app during inpatient stays. Second, participants expressed varying degrees of willingness to use the app during ED visits, particularly for low acuity ED visits. Third, participants stated their willingness to have their location tracked by the proposed app due to its perceived benefits. Fourth, the most frequently mentioned barriers to acceptability were inconveniences such as "false alarm" notifications and smartphone battery drainage. Finally, there was some tension between how to maximize usability without unnecessarily increasing user burden. Both clinicians and care managers expressed interest in receiving messages from the app at the time of hospital arrival and at discharge. Clinicians were particularly interested in conducting outreach during ED visits and inpatient stays, while care managers expressed more interest in coordinating postdischarge care. CONCLUSIONS: High-risk primary care patients in a safety net setting reported a willingness to utilize smartphone location tracking technology to facilitate care coordination. Further research is needed on the development and implementation of new smartphone-based approaches to care coordination.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".