A Qualitative Case Study of Smartphone-Connected Hearing Aids: Influences on Patients, Clinicians, and Patient‐Clinician Interactions
Bibliographic record
Abstract
BACKGROUND: Innovations in hearing aid technology influence clinicians and individuals who use hearing aids. Little research, to date, explains the innovation adoption experiences and perspectives of clinicians and patients, which matter to a field like audiology, wherein technology innovation is constant. By understanding clinician and patient experiences with such innovations, the field of audiology may develop technologies and ways of practicing in a manner more responsive to patients' needs, and attentive to society's influence. PURPOSE: The authors aimed to understand how new innovations influence clinician and patient experiences, through a study focusing on connected hearing aids. "Connected" refers to the wireless functional connection of hearing aids with everyday technologies like mobile phones and tablets. RESEARCH DESIGN: The authors used a qualitative collective case study methodology, borrowing from constructivist grounded theory for data collection and analysis methods. Specifically, the authors designed a collective case study of a connected hearing aid and smartphone application, composed of two cases of experience with the innovation: the case of clinician experiences, and the case of patient experiences. STUDY SAMPLE: The qualitative sampling methods employed were case sampling, purposive within-case sampling, and theoretical sampling, and culminated in a total collective case n = 19 (clinician case n = 8; patient case n = 11). These data were triangulated with a supplementary sample of ten documents: relevant news and popular media collected during the study time frame. DATA COLLECTION AND ANALYSIS: The authors conducted interviews with the patients and clinicians, and analyzed the interview and document data using the constant comparative method. The authors compared their two cases by looking at trends within, between, and across cases. RESULTS: The clinician case highlighted clinicians' heuristic-based candidacy judgments in response to the adoption of the connected hearing aids into their practice. The patient case revealed patients' perceptions of themselves as technologically competent or incompetent, and descriptions of how they learned to use the new technology. Between cases, the study found a difference in the response to how the connected hearing aid changed the clinician-patient relationship. While clinicians valued the increased time they spent "getting to know" their patients, patients experienced some frustration specific to the additional troubleshooting related to Bluetooth connectivity. Across cases, there was a resounding theme of "normalization" of hearing aids via their integration with a "normal" technology (mobile phones) and general lack of concern about privacy in relation to the smartphone application and its tracking and geotagging features. Both audiologists and patients credited the connected hearing aids with increased opportunities to participate more fully in everyday life. CONCLUSIONS: The introduction of smartphone-connected hearing aids influenced the identities and candidate profiles of hearing aid users, and the nature of time spent in clinical interactions, in important and interesting ways. The influence of connected hearing aids on patient experience and audiology practice calls for continued research and clinical consideration, with implications for clinical decision-making regarding hearing aid candidacy. Further study should look critically at normalization and possible unintended stigmatizing effects of making hearing aids increasingly discreet.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".