PatientSphere is a data-sharing platform that uses blockchain technology to ensure patients have control over their health information. It offers HIPAA-compliant data-sharing capabilities to healthcare providers, medical researchers, pharmaceutical companies and health insurers.
PatientSphere enables patients to pull together data from electronic medical records, mobile health applications, wearable devices, chatbots, informatics programs like Apple HealthKit, Google Fit and other sources into one dashboard, where it remains secure and simple to navigate and share.
Open Health Network has developed cloud-based middleware consisting of HIPAA-compliant distributed database that stores patient health data, patient identity management layer powered by blockchain. A metadata layer can be used to publish identified information and can be searched by entities such as pharmaceutical companies looking for clinical trials among others. Such data is in demand from medical researchers and pharma companies seeking out participants for clinical trials, health insurance and healthcare providers to increase adherence to treatments and more.
Health systems can use PatientSphere to design patient experiences aimed at helping patients adhere to courses of treatment, learn about their health, track medications or give consent for certain information to be shared among providers.
Health insurers can use the platform to incentivize healthy behaviors among their members, and researchers and pharmaceutical companies can create modules aimed at finding participants in clinical trials and increase adherence and retention rates.
Any type of chatbot can be rapidly developed on OHN chatbot platform. Conversations with patients can be captured and integrated into EMR/EHR if needed.
Medication Adherence module enables patients to configure reminders to refill and take their medications. Medications can be added by patients, caregivers or nurses. Escalation messages can be setup if system determines that patients are not taking their medications as prescribed.
A Diary screen asks patients to report how they feel daily, gathering data on diet, appetite, physical activity, sleep, and other relevant variables.
Create any type of advanced surveys; immediately deploy them and maintain them using OHN's content management tool.
Social Network serves highly relevant content for patients, filtered by specific disease, symptoms and treatments. Social features are designed to draw patients into an interactive experience, enable peer-to-peer support and patient engagement, increasing the likelihood that patients use the application consistently over longer periods of time.
Conversations with patients can be done via voice: medications reminders; surveys; appointment reminders; etc
Whether tracking growth in users over time or allowing an individual user to see their self-reported levels of physical activity by month, OHN offers a wealth of visual data representation options that let both organizations and individuals see and respond to patterns and trends at a glance.
Gamification features (badges, social support and notifications) prompt patients to come back to the Diary screen frequently. An Activity/Questionnaire feature serves patients a battery of questions specific to their conditions and symptoms, as well as questions on lifestyle, diet, socioeconomic stressors and other data points that may be determinants or risk factors of their disease. As questionnaires of this kind can often be long and tedious, resulting in low completion rates, our version of the questionnaire is broken into groups of questions that can be answered in any order, at any time. Questions are also presented in a way that can be answered in a few taps. Gamification features drive users to return to the questionnaire and, over time, fill in missing information.
Open Health Network’s platform can be customized to fit any patient-centered data collection requirements. Organizations can change the look and feel of the platform to reflect their own branding and customize content based on a specific disease or condition — without the need for coding. To provide a richer user experience, OHN also allows for integration with third party sensors and/or wearable tech such as FitBit, Nike+ FuelBand, Withings scales and blood pressure monitors or diet tracking apps such as MyFitnessPal.
Create user profiles; assign roles to control data access.
Open Health Network implements cutting-edge AI algorithms and Big Data Analytics algorithms to enhance its healthcare products.
OHN creates real-time analytics integrated with its mobile application platform that enable critical insights into how patients use mobile applications, what works and what does not. The user insights are correlated with health outcomes, and predictive models are constructed to link app usage behavior to real-world behavior change required for disease management and prevention.
OHN maintains a comprehensive HIPAA-compliant data warehouse for all data [active and passive] generated by app users. Data can be exported to EMRs, research systems such as RedCap and SAS, CDISC schema, or using custom ETL processes.
OHN conducts custom on-site analysis of the data using Hadoop, Spark and and leading tools in statistical computing, and presents results via easy-to-use real-time interactive dashboards and regular reports.
OHN integrates data generated by application users with the universe of publicly and privately available datasets, including U.S. Census bureau socio-economic data, Medicare data, EPA environmental pollution data, location and mobility patterns from Uber, and social media data. Resulting datasets enrich our understanding of health and disease and provide insights on potential future interventions.
OHN helped large pharma company understand how changes in the Medicare Part D legislation affect users of their drugs. To do so, OHN used public data on over 4 million Medicare claims to develop a statistical model of likely patients, their disease progression and likely future prescriptions. This allows pharma company to better structure their offerings and insurance formularies in specific disease states and increase their market share over competing drugs.
OHN has developed machine learning models to recognize symptoms and adverse events in social media and open text data enabling new ways of monitoring drug performance in the real world.
OHN has implemented an agent-based model of incidence of colon cancer and implications of policy interventions, enabling policy analysis of early screenings and interventions. The model is being extended to cover other disease states including heart disease and diabetes.
OHN has extensive experience with other AI techniques, including recommendation engines, rule-based and case-based reasoning systems, support vector machines, and natural language processing.