Experiences of Navigator, a Finnish patient-segmentation service, in primary care: A mixed-methods study

Aging and multimorbid populations burden health services worldwide. Segmenting patients with similar health service needs into different groups and guiding care providers to tailor services to these groups could reduce this burden. Methods of patient segmentation have been based on, e.g., databases. However, the Finnish patient-segmentation innovation Navigator (Suuntima) considers patients’ perspectives on their coping in everyday life, as well as professionals’ views of the patients’ state of health. The segmentation is based on questions. The resulting care pathway related to the group helps professionals to coordinate patients’ health care and patients to utilize appropriate services. This first part of Navigator’s validation study evaluates its feasibility and content and face validity. We assess the web-service’s user experiences at nurses’ appointments with diabetic patients, time consumption, and Navigator’s question relevance, comprehensiveness, and comprehensibility. This mixed-methods study uses user experience questionnaires for both patients and professionals, and semi-structured focus-group interviews for professionals. We used descriptive statistics in the quantitative data analysis of the questionnaire study and thematic analysis to identify the codes and themes in the interview data. All 304 Navigator queries were completed at appointments. Most patients found Navigator easy to use. It helped in considering their situation better and from new perspectives. Most patients did not find it too time-consuming. Most professionals found it easy to use and suitable for appointments and patient segmentation. The questions were easy and unambiguous, and they assisted in discussing new or sensitive issues. Most queries were completed in less than 19 mins and less time was used if the patient was assigned to the nurse. Thematic analysis raised five main themes: 1) Well-functioning web-service, 2) Stimulus for conversation and action, 3) Rationale to complete Navigator with a professional, 4) Training and experience ease the use of Navigator, and 5) Navigator's room for improvement. Subthemes were identified for three main themes. We consider Navigator’s feasibility and face validity to be favorable. We suggest user instructions and the clarification of concepts to support the questions’ comprehensibility. Some patients may benefit from a nurse’s presence when responding to Navigator’s questions.

Web-Based Patient Segmentation in Finnish Primary Care: Protocol for Clinical Validation of the Navigator Service in Patients With Diabetes

Background An aging population and increasing multimorbidity challenge health care systems worldwide. Patient segmentation aims to recognize groups of patients with similar needs, offer targeted services to these groups, and reduce the burden of health care. In this study, the unique Finnish innovation Navigator, a web-based service for patient segmentation, is presented. Both patients and health care professionals complete the electronic questionnaire concerning patients’ coping in everyday life and health state. Thus, it considers the patient perspective on self-care. One of four customership-strategy (CS) groups (self-acting, community, cooperating, and network) is then proposed in response to the answers given. This resulting strategy helps both professionals to coordinate patient health care and patients to utilize appropriate health services. Objective This study aims to determine the feasibility, validity, and reliability of the Navigator service in the segmentation of patients with diabetes into four CS groups in a primary care setting. Patient characteristics concerning demographic status, chronic conditions, disabilities, health-related quality of life, and well-being in different CS groups will be described. We hypothesize that patients in the network group will be older, have more illnesses, chronic conditions or disabilities, and require more health care services than patients in the self-acting group. Methods In this mixed methods study, data collection was based on questionnaires (user experience of Navigator, demographic and health status, World Health Organization Disability Assessment Schedule 2.0, EuroQol 5D, Wellbeing Questionnaire 12, and the Diabetes Treatment Satisfaction Questionnaire) issued to 300 patients with diabetes and on user-experience questionnaires for and semistructured focus-group interviews with 12 nurses. Navigator-database reports and diabetes-care values (blood pressure, BMI, HbA1c, low-density lipoprotein, albumin-creatinine, smoking status) were collected. Qualitative and descriptive analyses were used to study the feasibility, content, concurrent, and face validity of Navigator. While criterion and concurrent validity were examined with correlations, reliability was examined by calculating Cohen kappa and Cronbach alpha. Construct validity is studied by performing exploratory-factor analysis on Navigator data reports and by hypothesis testing. The values, demographics, and health status of patients in different groups were described, and differences between groups were studied by comparing means. Linear regression analysis was performed to assess which variables affect CS group variation. Results Data collection was completed in September 2019, and the first feasibility results are expected by the end of 2020. Further results and publications are expected in 2021 and 2022. Conclusions This is the first scientific study concerning Navigator’s psychometric properties. The study will examine the segregation of patients with diabetes into four CS groups in a primary care setting and the differences between patients in groups. This study will assist in Navigator’s further development as a patient segmentation method considering patients’ perspectives on self-care. This study will not prove the effectiveness or efficacy of Navigator; therefore, it is essential to study these outcomes of separate care pathways. International Registered Report Identifier (IRRID) DERR1-10.2196/20570

Web-Based Patient Segmentation in Finnish Primary Care: Protocol for Clinical Validation of the Navigator Service in Patients With Diabetes (Preprint)

BACKGROUND An aging population and increasing multimorbidity challenge health care systems worldwide. Patient segmentation aims to recognize groups of patients with similar needs, offer targeted services to these groups, and reduce the burden of health care. In this study, the unique Finnish innovation Navigator, a web-based service for patient segmentation, is presented. Both patients and health care professionals complete the electronic questionnaire concerning patients’ coping in everyday life and health state. Thus, it considers the patient perspective on self-care. One of four customership-strategy (CS) groups (self-acting, community, cooperating, and network) is then proposed in response to the answers given. This resulting strategy helps both professionals to coordinate patient health care and patients to utilize appropriate health services. OBJECTIVE This study aims to determine the feasibility, validity, and reliability of the Navigator service in the segmentation of patients with diabetes into four CS groups in a primary care setting. Patient characteristics concerning demographic status, chronic conditions, disabilities, health-related quality of life, and well-being in different CS groups will be described. We hypothesize that patients in the network group will be older, have more illnesses, chronic conditions or disabilities, and require more health care services than patients in the self-acting group. METHODS In this mixed methods study, data collection was based on questionnaires (user experience of Navigator, demographic and health status, World Health Organization Disability Assessment Schedule 2.0, EuroQol 5D, Wellbeing Questionnaire 12, and the Diabetes Treatment Satisfaction Questionnaire) issued to 300 patients with diabetes and on user-experience questionnaires for and semistructured focus-group interviews with 12 nurses. Navigator-database reports and diabetes-care values (blood pressure, BMI, HbA1c, low-density lipoprotein, albumin-creatinine, smoking status) were collected. Qualitative and descriptive analyses were used to study the feasibility, content, concurrent, and face validity of Navigator. While criterion and concurrent validity were examined with correlations, reliability was examined by calculating Cohen kappa and Cronbach alpha. Construct validity is studied by performing exploratory-factor analysis on Navigator data reports and by hypothesis testing. The values, demographics, and health status of patients in different groups were described, and differences between groups were studied by comparing means. Linear regression analysis was performed to assess which variables affect CS group variation. RESULTS Data collection was completed in September 2019, and the first feasibility results are expected by the end of 2020. Further results and publications are expected in 2021 and 2022. CONCLUSIONS This is the first scientific study concerning Navigator’s psychometric properties. The study will examine the segregation of patients with diabetes into four CS groups in a primary care setting and the differences between patients in groups. This study will assist in Navigator’s further development as a patient segmentation method considering patients’ perspectives on self-care. This study will not prove the effectiveness or efficacy of Navigator; therefore, it is essential to study these outcomes of separate care pathways. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/20570

Analysis of Segmentation, Reasons and Interests of Visiting Patients in the Inpatient Room at RSUD Genteng Banyuwangi City

Hospital health services today undergo a fundamental change, namely to become a business entity with several strategic business units that require handling with the right management concept. In order to be able to sell services that are in accordance with market conditions. The purpose of the study is to find out the patient segmentation and the reasons for patients visiting the hospital and the interest of patients returning to the Banyuwangi Hospital. The research design was Cross Sectional. The population is all inpatients. The sample size is 144 respondents using Simple Random sampling technique. Independent variable is Segmentation. The dependent variable is the reason for the patient to visit and the interest in revisiting. Data were collected using a questionnaire, data were analyzed using logistic regression test (α ≤ 0.05). The results showed that the largest segmentation variable, namely the marital status variable did not affect the interest in revisiting (p = 0.849), and the residential variable did not affect the interest in revisiting (p = 0.698). Whereas the reason for visiting the biggest variable, namely the price variable does not affect the interest in revisiting (p = 0.628), the place variable does not affect the interest in revisiting (p = 0.531), and the people variable does not affect re-interest (p = 0.722). Segmentation and patient reasons have no effect on interest in revisiting. The quality of service at the hospital must provide satisfying and attractive services to attract patients to return to visit

HE Stain Image Segmentation Using an Innovative Type-2 Fuzzy Set-Based Approach

HE stain images are widely used in medical diagnosis and often considered a gold standard for histology and pathology laboratories. A proper analysis is needed to have a critical decision about the status of the diagnosis of the concerned patient. Segmentation is always considered as an advanced stage of image analysis where objects of similar properties are put in one segment. But segmentation of HE stain images is not an easy task as these images involve a high level of fuzziness with them mainly along the boundary edges. So, traditional techniques like hard clustering techniques are not suitable for segmenting these images. So, a new approach is proposed in this chapter to deal with this problem. The proposed approach is based on type-2 fuzzy set and is new. The experimental results prove the superiority of the proposed technique.

Patient Segmentation Based on Patient Loyalty

The present study aims to perform a segmentation of patients based on their loyalty behaviour. The analysis focuses on Valencia, a region in Spain that features a capitated financing and free-elective framework; such a framework is particularly suitable for this type of study because patient loyalty directly affects the system’s budget and economic viability. Using secondary data from the regional health council, the study focuses on relationships of influence and latent segmentation in answering seven research questions. The two-pronged statistical analysis is designed to analyse relationships of influence, on the one hand, and latent segmentation, on the other. Significant differences were found among the various scales analysed in the three patient loyalty behavioural models (capture, retention and desertion) for each variable within the scope, that is, subjective (gender, age and nationality) and circumstantial (size of the assigned and receiving hospital, location of the province of the assigned hospital). This finding indicates that it may be possible to develop patient profiles based on such variables to analyse different loyalty behaviours in patients and the impact of hospital communication strategies on these behaviours. Patient loyalty is essential to the viability of a capitated health care financing and management system. Likewise, identifying patient profiles would contribute to a better Valencian public health management. Accordingly, it might be applied to evaluate other health care financing systems.

Evaluating patients' perception towards generic and branded medicines: A segmentation approach

The objective of this study is to identify the underlying subgroups of consumers in terms of their perceptions towards branded and generic medicines in emerging economies. To the best of our knowledge, no research till date has dwelled on their patient segmentation based on their psychographic towards medicine. This makes the current study a seminal attempt in its category. Based on the survey data collected by the authors from Delhi and National Capital region of India, the present research employs consumer research methodologies. Cluster analysis based on psychographics and demographic was employed to cluster consumers based on their perception towards and generic medicines. The cluster-based analysis segmented the patients into three categories namely Branded Medicine Inclined, Generic Medicine inclined, Cost Conscious. From the extant review of the literature, it was observed that segmentation of patients based on their perceptions was found to be insignificant. Identifying and establishing patient clusters will help the government agencies in devising and managing healthcare awareness program towards generic medicines in an efficient fashion.