The digital era has allowed for the creation of positive patient experiences by bridging data across the patient’s journey. In order to personalize a patient’s experience, many factors need to be identified that impact health and well-being, not just physical but behavioral as well. From googling symptoms, looking for professionals, getting appointments to payment, personalization helps in timing, call to action, interactions, the way content is delivered, etc.
The social determinants of health and behavioral patterns of individuals are relatively new areas of study. AI comes in to support to provide information regarding factors for successful treatment and wellbeing and provide assistance. The data gap exists because healthcare organizations leave valuable data unutilized, because of complex systems, data in different formats and other problems. The goal must be to use data in better holistic way to impact patients while also considering variances in context of care or personas, generational differences, cultural considerations, administrative competency, competing priorities, regulatory constraints, security concerns and other issues.
In the process of democratization of data, the first step is collection of data, making relevant data available to healthcare professionals in order to use the information of same person in different circumstances. The next step is aggregation of information for a population and defining services and staffing needs on an individual layer and organization layer. All aspects including social are combined into a single data driven website to offer a streamlined way to search for services, make appointments.
A comprehensive approach must be taken and security standards and compliance certifications must be followed as certain patients may require higher security measures. The shared responsibility model relieves the customer’s operational burden and controls components such as host operating system, virtualization, physical security, etc.
Latest technology innovation integration enables personalized care model. From the person feeling sick to receiving healthcare assistance is multiple steps. The integration of AI reduces number of steps as it enables remote access, gives information and results through online portals, provides data regarding better engagement with patients of a specific community and brings digital solutions.
Personalized care models improve retention, bring more referrals, improve risk adjustment, reduces out of pocket costs, customer service is improved and quality of care improves. Care stakeholders can interject and communicate with the system as well. Personalized care models increase brand awareness and brand loyalty and results in a better network of providers. Patients can be seen as whole individuals not just specific interactions. It helps the organization holistically impact the patient and gives a clear view to help engagement to result in better outcomes.
The personalization of care models creates a self-sustaining loop leading to a snow ball effect. It starts with data, which is freed up, thus democratizing the data, creating liquidity, leading to better personalization of patient interactions. This in turn leads to more automation, bringing in more data to the organization, thus creating a loop. Therefore, it is imperative for healthcare organizations to begin the process of personalization of care models.
