The aim of patient profiling is to enable care providers to provide the right care, to the right person, at the right time. It draws on the concept of ‘mass customization’, where goods and services are delivered to a large number of clients with enough variety and customization that nearly everyone finds exactly what they want . Starbucks, Levi’s and Burger King are prominent examples of companies that have implemented this concept of targeting ‘markets of a few’ . At Starbucks, for example, customers can customize their coffee by choosing from a variety of sizes, flavours and toppings. In healthcare, mass customization is less well known, but with many patients with specific diseases that have varying care needs, abilities and preferences, it could be a solution for delivering more tailored healthcare.
Patient profiling uses the individual’s preferences to tailor the content, context and delivery mode of care to improve care experience and health outcomes [14,15,16], including quality of life, as well as reducing the per capita costs of care. The development of the tailored care based on profiles consists of four steps: (1) identification of the target population, (2) assessment, (3) stratification, and (4) tailoring (Fig. 1). After defining the population (e.g. patients with type 2 diabetes treated in primary care), care providers assess relevant phenotypic patient characteristics, such as body weight, quality of life and self-efficacy, which are predictive of relevant outcomes, such as glycaemic control and patient satisfaction. Subsequently, these characteristics are used to stratify patients into profiles. This approach results in subgroups of patients who are more homogeneous than the population as a whole in terms of care needs, abilities and preferences, while acknowledging that a certain amount of heterogeneity within these subgroups will remain. In the last step, the patient’s care is adapted depending on his or her profile.
Comparison of Two Patient Profiles Studies
In the following section, two ongoing research projects that use the modus operandi as described above are explained. Both projects apply different techniques to do so. One uses a quantitative approach and the other a mixed-method approach. The current conceptual article is based on the two projects and does not directly contain any studies with human participants or animals performed by any of the authors for which ethical approval was required. An overview of both approaches can be found in Table 1.
Patient Profiles: A Quantitative Approach
The Dutch PROFILe (PROFiling patients’ healthcare needs to support Integrated, person-centred models for Long-term disease management) project started in 2014 and is a 4-year public–private research collaboration between a university, hospital, pharmaceutical company and two diabetes care networks (DCNs). PROFILe aims to develop, validate and test patient profiles as an instrument for tailored diabetes management in primary care . The two DCNs both routinely collect patient data. One DCN was considered the development cohort (n = 10,528) and the other the validation cohort (n = 3777).
A quantitative approach was used to develop the patient profiles. In the first step, the longitudinal electronic health records of the development cohort were used to conduct growth mixture modelling . This technique identified three subgroups of patients based on glycaemic control trajectories starting from the point of diagnosis: (1) stable, adequate glycaemic control; (2) improved glycaemic control and (3) deteriorated glycaemic control. Glycaemic control trajectories were chosen as the outcome, because the researchers hypothesized that patients with different glycaemic control trajectories prefer different configurations of diabetes care and support. The identified subgroups were validated in the validation cohort. Second, to explore which phenotypic patient characteristics should be assessed to determine a patient profile and to stratify patients into the right trajectory, machine learning methods were applied. Using the most salient characteristics (baseline body mass index, HbA1c and triglycerides), an algorithm was built to predict the identified glycaemic control trajectories, which was subsequently validated in the validation cohort. The project is currently on the third step, ‘tailoring’: the adaption of care per patient profile. A so-called discrete choice experiment (DCE) is conducted among 300 patients to provide insight into the patients’ preferences for specific configurations of diabetes care and support (e.g. frequency of professional monitoring, involved providers, information provision). These care preferences are paired with the corresponding patient profiles. To diminish heterogeneity within each profile, the influence of psychosocial characteristics, such as self-efficacy and quality of life, on the preferences is also determined.
In the final step of the PROFILe project, a clustered randomized controlled trial will be performed at primary care practices in the Netherlands to assess the perceived benefits, risks and the feasibility of implementing patient profiles as an instrument to safely and successfully provide tailored type 2 diabetes management.
Patient Profiles: A Mixed-Method Approach
The Tailored Healthcare Through Customer Profiling project is a 4-year public–private research collaboration between a hospital, medical device manufacturer, technical university and the creative industry. Its main aims are to define a validated set of design-oriented patient profiles and to test the effect of integrating these profiles in healthcare services (e.g. educational materials and telerehabilitation systems) on satisfaction with care provision following joint replacement surgery.
A mixed-method approach was used to develop the profiles. As a first step, self-reported communication preferences, experiences with pain and stress, self-efficacy, clinical symptoms and surgical outcomes of patients who had underwent joint replacement surgery were assessed. To stratify patients in groups with similar preferences and experiences, k-means cluster analysis was used. The resulting subgroups were validated by comparing the average subgroup characteristics to patients’ actual and ideal hospital experience as expressed in qualitative interviews. To ease classification of future patients into the relevant subgroup by health professionals, recursive partitioning was used to build a decision tree . By asking three questions (which assess active coping skills, experienced helplessness, information needs) either during the consultation or via a self-reported questionnaire, health professionals can quickly stratify future patients to one of the subgroups and deliver care that is better aligned to the patient’s preferences, even when constrained by time.
The final ‘tailoring’ step in this project consists of developing modular variations of existing patient education materials and supportive telerehabilitation systems by design engineers. From their iterative work, it will be determined how preferences should be embedded in tailored design. The envisioned benefit of profile usage (i.e. improved satisfaction) will be examined in a pilot validation of the developed tailored prototypes.