Personalization is an expected standard for today’s patients.
To acquire a personal understanding of a healthcare consumer, providers and health plan administrators must have access and insight into every area of a healthcare journey across physical and digital channels. In-person appointments, care gaps, claims, benefits eligibility, chronic condition management, medication adherence, telehealth, and social determinants of health (SDOH) are all elements of a typical healthcare journey, and meeting patient expectations for a personalized healthcare experience requires a consistent approach across every touchpoint.
Personalization is an expected standard for today’s patients. According to a recent Dynata survey of healthcare consumers, 66% of patients surveyed said they would choose a provider based on the provider’s ability to communicate in a consistent and timely manner, and 60% said it is critical for a provider to show how well they understand an individual – beyond basic patient data.
The challenge for many healthcare professionals, however, is data is often siloed by channel, process or line of business, clouding a single view of the patient and making it extraordinarily difficult to engage with consistency in an omnichannel fashion. For example, a provider may implement a marketing campaign to close care gaps. However, having protected health information (PHI) data siloed from personally identifiable data (PII) and other patient information (like demographics) makes it nearly impossible to segment an audience based on a specific health condition, or deliver a campaign that takes into account a patient’s channel or content preferences.
Instead, a typical campaign might send static content based solely on a patient’s age, location or another trait that might not be relevant to the patient’s current healthcare journey. The patient may, for instance, have recently had a preventive screening but because they’re on an email list they receive a message asking them to schedule the same screening. Now the patient is confused if not outright angry that the provider does not seem to know anything about them.
Automated machine learning
Machine learning in an integrated customer data platform that combines PHI data, PII data, consumer, claims and clinical data in a single platform solves for the siloed data problem, allowing healthcare professionals to segment audiences at a granular level and vastly increase relevance for almost every type of patient engagement.
When self-training, automated machine learning models continually run in production and are tuned to address a specific business goal (closing care gaps, retention, acquisition, etc.), inbound and outbound interactions can be optimized at an individual patient level, tailored by channel, health condition, SDOH or whatever will most directly impact the desired goal.
In a campaign to close a care gap, a model that includes all patient data, updated in real time, will produce segments optimized for and precisely aligned with an individual patient’s current healthcare journey. Irrelevant and untimely messages or communications become a thing of the past, as do communications in the wrong channel. Instead, each communication – inbound or outbound – is pitch perfect to a patient or healthcare consumer’s current situation and demonstrates a full patient understanding.
Understanding a patient’s social determinants of health, for example – the patient’s access to a nutritious diet, proximity to greenspace, access to transportation – is vital knowledge in a value-based care (VBC) compensation model, and the ability to segment an audience based on that level of detail is extremely valuable for creating hyper-relevant communications that succeed in connecting patients with their doctor.
Single point of control
Because healthcare outreach demands precision, an integrated customer data platform should have a rules-based approach to database extractions, where audiences are created and defined through a set of logic – a rule – which is applied to a campaign and evaluated at each point in the campaign where a list is typically used.
When a rule is evaluated at each inflection point, such as the sending of an email or direct mailer, an inbound call to the call center, etc., the extract happens at the latest point possible, which eliminates the staleness of a list that begins to lose relevance as soon as it is created. Rules can also be created at the enterprise level and re-used at the campaign level, creating consistency in the definition of rules as well as operational efficiencies. When a rules-based approach is combined with a real-time consolidated patient view, precision is guaranteed.
It's a fundamentally different approach than cutting a list, which provides marketers and business users with a point-in-time view of a patient. As soon as a list is created, however, it immediately loses relevance because it discards any new data that might inform how a patient is progressing through a healthcare journey.
A customer data platform that offers a single point of operational control will also have an intelligent orchestration layer that makes it possible to package up segments as a whole, creating content packages that are reusable and extensible across the entire channel ecosystem. The right patient is then matched to the right communication every time, on any channel, inbound or outbound.
A patient-centric transformation
In the Gartner Generations Model for planning and executing healthcare consumer engagement initiatives, demonstrating a personal understanding of a patient that meets patient expectations for a holistic, omnichannel healthcare experience is farther along than where most healthcare organizations sit today.
A true “patient-centric” approach is one in which a healthcare consumer has access to and control of their health data from across the entire healthcare ecosystem. A unified patient record in this model will ingest data from every source and be available and accessible to providers, insurers, caregivers, devices, etc. There are no data, process, department or channel siloes that contribute to a disjointed patient experience.
Most healthcare organizations are in Generation 1, where they may use patient or consumer data to optimize a journey across a single channel. To progress, organizations and then the entire healthcare ecosystem must begin to act as one on behalf of the patient. To start along the generational path, one recommendation is to evolve from assigning initiatives to a single business unit (such as a home healthcare aid responsible for chronic care management) to coordinating patient care across the enterprise, with all departments accessing the same data to create a more seamless experience.
An integrated customer data platform that supports a transformation to a patient-centric model will ingest all patient data into a single platform, provide business users with a single point of operational control and through built-in machine learning will intelligently orchestrate personalized experiences at scale across the entirety of omnichannel healthcare journeys.
Chris Evanguelidi, Director of the Enterprise Healthcare Market for Redpoint Global, is focused on helping healthcare enterprises harness the value of data to drive healthcare consumerism by leveraging the brightest minds, best practices and proven leading edge solutions.