Communication Behavior Changes Between Patients With Diabetes and Healthcare Providers Over 9 Years: Retrospective Cohort Study.

BACKGROUND: Health organizations and patients interact over different communication channels and are harnessing digital communications for this purpose. Assisting health organizations to improve, adapt, and introduce new patient-health care practitioner communication channels (such as patient portals, mobile apps, and text messaging) enhances health care services access. OBJECTIVE: This retrospective data study aims to assist health care administrators and policy makers to improve and personalize communication between patients and health care professionals by expanding the capabilities of current communication channels and introducing new ones. Our main hypothesis is that patient follow-up and clinical outcomes are influenced by their preferred communication channels with the health care organization. METHODS: This study analyzes data stored in electronic medical records and logs documenting access to various communication channels between patients and a health organization (Clalit Health Services, Israel). Data were collected between 2008 and 2016 from records of 311,168 patients diagnosed with diabetes, aged 21 years and over, members of Clalit at least since 2007, and still alive in 2016. The analysis consisted of characterizing the use profiles of communication channels over time and used clustering for discretization purposes and patient profile building and then a hierarchical clustering and heatmaps to visualize the different communication profiles. RESULTS: A total of 13 profiles of patients were identified and characterized. We have shown how the communication channels provided by the health organization influence the communication behavior of patients. We observed how different patients respond differently to technological means of communication and change or don't change their communication patterns with the health care organization based on the communication channels available to them. CONCLUSIONS: Identifying the channels of communication within the health organization and which are preferred by each patient creates an opportunity to convey messages adapted to the patient in the most appropriate way. The greater the likelihood that the therapeutic message is received by the patient, the greater the patient's response and proactiveness to the treatment will be. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/10734.

Trends in the Prevalence of Chronic Medication Use Among Children in Israel Between 2010 and 2019: Protocol for a Retrospective Cohort Study.

BACKGROUND: Prescription of psychostimulants has significantly increased in most countries worldwide for both preschool and school-aged children. Understanding the trends of chronic medication use among children in different age groups and from different sociodemographic backgrounds is essential. It is essential to distinguish between selected therapy areas to help decision-makers evaluate not only the relevant expected medication costs but also the specific services related to these areas. OBJECTIVE: This study will analyze differences in trends regarding medications considered psychobehavioral treatments and medications considered nonpsychobehavioral treatments and will identify risk factors and predictors for chronic medication use among children. METHODS: This is a retrospective study. Data will be extracted from the Clalit Health Services data warehouse. For each year between 2010 and 2019, there are approximately 1,500,000 children aged 0-18 years. All medication classes will be identified using the Anatomical Therapeutic Chemical code. A time-trend analysis will be performed to investigate if there is a significant difference between the trends of children's psychobehavioral and nonpsychobehavioral medication prescriptions. A logistic regression combined with machine learning models will be developed to identify variables that may increase the risk for specific chronic medication types and identify children likely to get such treatment. RESULTS: The project was funded in 2019. Data analysis is currently underway, and the results are expected to be submitted for publication in 2022. Understanding trends regarding medications considered psychobehavioral treatments and medications considered nonpsychobehavioral treatments will support the identification of risk factors and predictors for chronic medication use among children. CONCLUSIONS: Analyzing the response of the patient (and their parents or caregivers) population over time will hopefully help improve policies for prescriptions and follow-up of chronic treatments in children. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36756.

Personalized Health Systems-Past, Present, and Future of Research Development and Implementation in Real-Life Environment.

Personal health systems (PHS) are designed to provide the individual with tailored care while enabling the healthcare system to deliver high-quality care to large populations and maintain a sustainable system. Solutions using electronic health records (EHRs) that include predictive models for the risk of disease onset and deterioration enable the care provider to better identify and treat patients with chronic disease and provide personalized prevention. These tools are well-accepted by doctors and have been proven to improve health outcomes and reduce costs. Integrated telecare programs were implemented for comorbid patients showing improved clinical outcomes self-management and quality of life (QoL). However, different patient populations benefit in different ways from these care plans, and thus, continuous evaluation, service adaptation in a real-life environment set with clear outcome measures, is required for best results. The challenge of the PHS today is to acquire patient-generated data (PGD) and behavioral and patient-reported outcomes (PROs) for PHS development that can be combined with existing clinical data. Some initiatives of healthcare organizations [health maintenance organizations (HMOs)] in Israel demonstrate how this goal can be achieved with relatively small efforts by using a stepwise and agile approach to service implementation that improve service by enabling adoption and adaptation of the service in the short term while collecting data for advanced PHS development in the long term. This approach, combined with programs and incentive payments at the national level, creates an environment and infrastructure for collaboration between healthcare, academia, and industry for research, development, and implementation of future PHS. This article presents examples of PHS development and implementation from the Israeli healthcare system. We discuss the lessons learned and suggest new approaches for research, development implementation, and evaluation of PHS that will address the needs of future healthcare.

Identification and Description of Healthcare Customer Communication Patterns Among Individuals with Diabetes in Clalit Health Services: A Retrospective Database Study.

HMOs record medical data and their interactions with patients. Using this data we strive to identify sub-populations of healthcare customers based on their communication patterns and characterize these sub-populations by their socio-demographic, medical, treatment effectiveness, and treatment adherence profiles. This work will be used to develop tools and interventions aimed at improving patient care. The process included: (1) Extracting socio-demographic, clinical, laboratory, and communication data of 309,460 patients with diabetes in 2015, aged 32+ years, having 7+ years of the disease treated by Clalit Healthcare Services; (2) Reducing dimensions of continuous variables; (3) Finding the K communication-patterns clusters; (4) Building a hierarchical clustering and its associated heatmap to summarize the discovered clusters; (5) Analyzing the clusters found; (6) Validating results epidemiologically. Such a process supports understanding different communication-channel usage and the implementation of personalized services focusing on patients' needs and preferences.