Hospitals' Adoption of Mobile-Based Personal Health Record Systems and Patients' Characteristics: A Cross-Sectional Study Analyzing National Healthcare Big Data.
Young-Taek ParkHyeoun-Ae ParkJae Meen LeeByung Kwan ChoiPublished in: Inquiry : a journal of medical care organization, provision and financing (2023)
Insufficient information exists on the associations between hospitals' adoption of mobile-based personal health record (mPHR) systems and patients' characteristics. This study explored the associations between patients' characteristics and hospitals' adoption of mPHR systems in Korea. This cross-sectional study used 316 hospitals with 100 or more beds as the unit of analysis. Previously collected data on mPHR adoption from May 1 to June 30, 2020 were analyzed. National health insurance claims data for 2019 were also used to analyze patients' characteristics. The dependent variable was mPHR system adoption (0 vs 1) and the main independent variables were the number of patients, age distribution, and proportions of patients with cancer, diabetes, and hypertension among inpatients and outpatients. The number of inpatients was significantly associated with mPHR adoption (adjusted odds ratio [aOR]: 1.174; 1.117-1.233, P < .001), as was the number of outpatients (aOR: 1.041; 1.028-1.054, P < .001). The proportion of inpatients aged 31 to 60 years to those aged 31 years and older was also associated with hospital mPHR adoption (aOR: 1.053; 1.022-1.085, P = .001). mPHR system adoption was significantly associated with the proportion of inpatients (aOR: 1.089; 1.012-1.172, P = .024) and outpatients (aOR: 1.138; 1.026-1.263, P = .015) with cancer and outpatients (aOR: 1.271; 1.101-1.466, P = .001) with hypertension. Although mPHR systems are useful for the management of chronic diseases such as diabetes and hypertension, the number of patients, younger age distribution, and the proportion of cancer patients were closely associated with hospitals' introduction of mPHR systems.
Keyphrases
- healthcare
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- electronic health record
- big data
- peritoneal dialysis
- blood pressure
- public health
- cardiovascular disease
- mental health
- squamous cell carcinoma
- risk assessment
- patient reported outcomes
- young adults
- skeletal muscle
- glycemic control
- drug induced
- arterial hypertension