Patterns of Patients’ Interactions With a Health Care Organization and Their Impacts on Health Quality Measurements: Protocol for a Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Data collected by health care organizations consist of medical information and documentation of interactions with patients through different communication channels. This enables the health care organization to measure various features of its performance such as activity, efficiency, adherence to a treatment, and different quality indicators. This information can be linked to sociodemographic, clinical, and communication data with the health care providers and administrative teams. Analyzing all these measurements together may provide insights into the different types of patient behaviors or more accurately to the different types of interactions patients have with the health care organizations. OBJECTIVE: The primary aim of this study is to characterize usage profiles of the available communication channels with the health care organization. The main objective is to suggest new ways to encourage the usage of the most appropriate communication channel based on the patient's profile. The first hypothesis is that the patient's follow-up and clinical outcomes are influenced by the patient's preferred communication channels with the health care organization. The second hypothesis is that the adoption of newly introduced communication channels between the patient and the health care organization is influenced by the patient's sociodemographic or clinical profile. The third hypothesis is that the introduction of a new communication channel influences the usage of existing communication channels. METHODS: All relevant data will be extracted from the Clalit Health Services data warehouse, the largest health care management organization in Israel. Data analysis process will use data mining approach as a process of discovering new knowledge and dealing with processing data extracted with statistical methods, machine learning algorithms, and information visualization tools. More specifically, we will mainly use the k-means clustering algorithm for discretization purposes and patients' profile building, a hierarchical clustering algorithm, and heat maps for generating a visualization of the different communication profiles. In addition, patients' interviews will be conducted to complement the information drawn from the data analysis phase with the aim of suggesting ways to optimize existing communication flows. RESULTS: The project was funded in 2016. Data analysis is currently under way and the results are expected to be submitted for publication in 2019. Identification of patient profiles will allow the health care organization to improve its accessibility to patients and their engagement, which in turn will achieve a better treatment adherence, quality of care, and patient experience. CONCLUSIONS: Defining solutions to increase patient accessibility to health care organization by matching the communication channels to the patient's profile and to change the health care organization's communication with the patient to a highly proactive one will increase the patient's engagement according to his or her profile. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/10734.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".