Inform Personalization to Elevate Medication Adherence

Inform Personalization to Elevate Medication Adherence

Co-authored by: Kaley Simon SVP, Product, MedAdvisor Solutions US

You may also be interested to read Personalize Patient Messaging to Improve Medication Adherence for foundational concepts that inform advances in developing personalized adherence intervention strategies. It offers valuable context that enhances the methodologies and innovations we discuss below.

Quandary Peak Research

Introduction

Patient consumerism, given the abundance of information technology, is ever evolving. The present-day patient seeks personalized and integrated knowledge to aid their decision-making, in particular, information that is specific to their current circumstances and needs. For the healthcare industry, this shift now necessitates the need to gain an in-depth understanding of the patient’s decision-making processes to optimize engagement strategies. Patient personas and segmentation analysis results in creation of homogenous cohorts with specific needs, characteristics, behaviors and motivations, and informs a 360° view into their decision making process. Collectively, they inform delivery of impactful personalized medication adherence intervention strategies.

Patient Segmentation Models

The core objective of segmentation is to assemble individuals sharing common traits, facilitating precise delivery of care and interventions. Historically, patient segmentation models have narrowly focused on characteristics such as patient age and disease state, or physical status. Successful interventions, the goal of segmentation, requires a panoramic view of the patient psyche and the environmental conditions impacting them. The availability of diversified data enables these holistic patient insights that go beyond adherence barriers to also include past behaviors, attitudes, motivations, and priorities and help depict a near-perfect characterization of the distinct patient cohorts within the target population. We recommend organizations to incorporate machine-learning computational approaches to patient segmentation, one that takes into account socioeconomic and demographic, psychographic, behavioral, and disease, health and prescription history datasets. In doing so, we do not prescribe that organizations replace existing expert-opinion derived segmentation methodologies but instead utilize computational methodologies to better inform, add to, or further validate human intelligence.

Pertinent dataset categories and example variables within them

Patient Personas Inform Segmentation And Vice Versa

A persona is a fictional, yet credible archetype that helps to depict the experiences, motivations, and goals of a group of patients, as well as the barriers they face. Patient personas are summarized illustrations of niche audiences within the population cohort that share common personal attributes, needs, and behavioral characteristics such as motivations, attitudes, and pain points. Personas should be crafted with a keen focus on the overarching business goals, underpinned by thorough research and insight. With careful attention, properly constructed patient personas can precisely inform the required interventions complete with key words, phrases, propositions and channel preferences to ensure personalization and, therefore, optimal patient engagement. Analysis of data using machine learning methods, such as clustering or classification techniques, can also inform creation of new and distinct patient personas that might have been overlooked but demand a unique cohort-specific intervention strategy to affect change.?

Patient Personas Help Build the Patient Journey Maps?

Patient journey mapping, an adopted marketing science decision thinking methodology, describes the patient’s end-to-end experience with healthcare services they receive. Patient journey maps would begin by first documenting the patient’s initial need to seek care for a particular health concern and then progress through information gathering, choice analysis including provider inputs, a subsequent decision to procure the medication, and follow-on experiences. Impactful patient journey maps, presented as visual representations, must accurately depict genuine end-to-end patient experiences that either inform opportunities for course corrections (based on bad experiences) or further engagement (based on good experiences). It is important that patient journeys are not limited to an understanding of the patient’s satisfaction at the various engagement touch points (interactions) but should holistically capture their entire end-to-end experience.

Personas inform the creation of patient journey maps to document their experiences. Patient journey mapping is a team exercise, one that is rooted in empirical research based on the customer voice. Consider these critical ingredients while developing patient journey maps:

  1. Contextual: understand the place of interaction and the moment in the journey to influence the next touch point interaction?
  2. Emotional: capture the emotional mindset at every step to inform course corrections and adjust messaging
  3. Moments of Truth: identify key touchpoint in the journey that hold the greatest potential to delight or distance the patient

When done right, journey maps can help to better understand the patient perspective which in turn can unravel engagement opportunities or identify critical actions to resolve pain points.

The Role of Baseline Assessments & Feedback Loop Mechanisms?

Availability of pertinent patient datasets along with advanced computational abilities now enables us to create homogenous patient cohorts and deliver impactful and targeted interventions. However, patients are humans and will change over time. We recommend, when possible, to conduct baseline assessments using survey techniques to validate and confirm computational segmentation outputs. Conducting a baseline assessment helps to assess the status quo at the start of the program and monitor progress.

Separately, we strongly encourage designing programs that inherently include helpful patient feedback loop systems to improve the performance of the patient segmentation model over time, better inform patient personas and journey maps, and fine tune or adjust implemented adherence intervention strategies to ensure personalization. While analyzing patient feedback, careful review of feedback received at the “moments of truth” touchpoints is necessary to impact change. Lastly, patient feedback loops can only be effective if they are promptly acted upon. A cross-departmental team should be assembled and empowered to execute rapid deployment of corrective actions.

Closing

Modern computing abilities and enriched patient datasets present the ability to identify distinct and homogenous patient cohorts and deliver tailored impactful interventions. Creation of patient personas can inform the training of segmentation models and optimize their outcomes. More importantly, patient personas and journey mappings help to discover the unique nuances presented in a patient population which can inform optimal personalized patient interventions. Patients are humans with unique preferences and contexts and regularly engaging them is imperative to optimize personalization and adherence intervention strategies.


References

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