Analysis of Requirements for Developing an mHealth-Based Health Management Platform
Bibliographic record
Abstract
BACKGROUND: Studies have consistently shown that mobile and Web-based apps have positive impacts on people's daily lifestyles, health management, and disease treatment. As the development of medical and health informatization in China has evolved, different kinds of mobile-based apps for individuals and hospitals have been developed by software vendors. However, doubts and challenges posed by the media have prevented these apps from having a stable and substantial user base. Analyses of user requirements have not typically been performed prior to the design of such mobile apps. The health information government authority in Liaoning Province, China, was planning to establish a mobile health (mHealth)-based health management platform, aiming to alleviate the difficulties citizens have in seeking hospital services. OBJECTIVE: The goal of this study was to determine the actual health and medical needs of citizens that may be addressed by medical information technologies. The results may contribute to the functional design and development of health management and appointed treatment-oriented mobile apps. METHODS: In this study, a semi-structured questionnaire on mHealth requirements was designed and tested, and 240 questionnaires were given to the outpatients of the First Hospital of the China Medical University in Shenyang, Liaoning Province, China; of these, 228 valid responses were collected, for a response rate of 95%. We discussed the current development of mHealth with 50 related experts and engineers from health authorities and a medical information company. SPSS 13.0 was used for statistical analyses. RESULTS: After detailed analyses of the questionnaire data, several findings were evident: first, most citizens and patients were unclear about their health conditions (64.5%, 147/228) and were interested in receiving a mobile app as a tool to manage their health and medical needs (71.1%, 162/228). Patients in different outpatient departments had different opinions regarding online registration. Conversely, the main problems for outpatients were long waiting times (66.4%, 148/223) and difficulties in making appointments (46.5%, 106/228), and they also worried about payments and Internet problems when using a mobile app for appointment reservations. Furthermore, as the main service target of mHealth is the health management of the general population, we first need to solve the associated interoperability and data security problems associated with such apps. CONCLUSIONS: This study provides insight into the health and medical requirements of smartphone apps, and draws attention to some of the challenges and opportunities of mHealth. We suggest several value-added features and characteristics that app developers should take into consideration when developing health and medical-related apps. The findings also highlight some major challenges that require further consideration and research to ensure that these apps meet the core needs of patients and aid the development of the health information system in Liaoning Province, China.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".