Evaluation of Healthcare Utilisation and Expenditures in Persons with Type 2 Diabetes Undergoing Bariatric-Metabolic Surgery.

PURPOSE: Changes in healthcare utilisation and expenditures after bariatric-metabolic surgery (BMS) for people with type 2 diabetes mellitus (T2DM) are unclear. We used the Dutch national all-payer claims database (APCD) to evaluate utilisation and expenditures in people with T2DM who underwent BMS. METHODS: In this cohort study, patients with T2DM who had BMS in 2016 were identified in the APCD. This group was matched 1:2 to a control group with T2DM who did not undergo BMS based on age, gender and healthcare expenditures. Data on healthcare expenditures and utilisation were collected for 2013-2019. RESULTS: In total, 1751 patients were included in the surgery group and 3502 in the control group. After BMS, total median expenditures in the surgery group stabilised (€ 3156 to € 3120) and increased in the control group (€ 3174 to € 3434). Total pharmaceutical expenditures decreased 28% in the surgery group (€957 to €494) and increased 55% in the control group (€605 to €936). In the surgery group, 67.1% did not use medication for T2DM in 2019 compared to 13.3% in the control group. Healthcare use for microvascular complications increased in the control group, but not in the surgery group. CONCLUSION: BMS in people with T2DM stabilises healthcare expenditures and decreases medication use and care use for microvascular complications. In contrast, healthcare use and expenditures in T2DM patients who do not undergo surgery gradually increase over time. Due to the progressive nature of T2DM, it is expected that these differences will become larger in the long-term.

Delineating the Type 2 Diabetes Population in the Netherlands Using an All-Payer Claims Database: Specialist Care, Medication Utilization and Expenditures 2016-2018.

OBJECTIVES: The aim of this study was to describe the healthcare utilization and expenditures related to medical specialist care and medication of the entire type 2 diabetes population in the Netherlands in detail. METHODS: For this retrospective, observational study, we used an all-payer claims database. Comprehensive data on specialist care and medication utilization and expenditures of the type 2 diabetes population (n = 900,522 in 2018) were obtained and analyzed descriptively. Data were analyzed across medical specialties and for various types of diabetes medication (or glucose-lowering drugs [GLDs]) and other medication. RESULTS: Specialist care utilization was diverse: different medical specialties were visited by a considerable fraction of the type 2 diabetes population. Total expenditures on specialist care were €2498 million in 2018 (i.e., 10.6% of the national specialist care expenditures). In total, 97.8% of patients used other medication (not GLDs) and 81.8% used GLDs; 25.6% of medication expenditures were for GLDs. For both specialist care and medication, mean expenditures per treated patient were higher than median expenditures, indicating a skewed distribution of spending. CONCLUSION: Use of and expenditures on specialist care and medication of the type 2 diabetes population is diverse. These heterogeneous healthcare use patterns are likely caused by the presence of comorbidities. Additionally, we found that a small fraction of the population is responsible for a large share of the expenditures. A shift towards more patient-centered care could lead to health improvements and a reduction in overall costs, subsequently promoting the sustainability of healthcare systems.

Identifying and delineating the type 2 diabetes population in the Netherlands using an all-payer claims database: characteristics, healthcare utilisation and expenditures.

OBJECTIVES: We aimed to identify and delineate the Dutch type 2 diabetes population and the distribution of healthcare utilisation and expenditures across the health system from 2016 to 2018 using an all-payer claims database. DESIGN: Retrospective observational cohort study based on an all-payer claims database of the Dutch population. SETTING: The Netherlands. PARTICIPANTS: The whole Dutch type 2 diabetes population (n=900 522 in 2018), determined based on bundled payment codes for integrated diabetes care and medication use indicating type 2 diabetes. OUTCOME MEASURES: Annual prevalence of type 2 diabetes, comorbidities and characteristics of the type 2 diabetes population, as well as the distribution of healthcare utilisation and expenditures were analysed descriptively. RESULTS: In 2018, 900 522 people (6.5% of adults) were identified as having type 2 diabetes. The most common comorbidity in the population was heart disease (12.1%). Additionally, 16.2% and 5.6% of patients received specialised care for microvascular and macrovascular diabetes-related complications, respectively. Most patients with type 2 diabetes received pharmaceutical care (99.1%), medical specialist care (97.0%) and general practitioner consultations (90.5%). In total, €8173 million, 9.4% of total healthcare expenditures, was reimbursed for the type 2 diabetes population. Medical specialist care accounted for the largest share of spending (38.1%), followed by district nursing (12.4%), and pharmaceutical care (11.5%). CONCLUSIONS: All-payer claims databases can be used to delineate healthcare use: this insight can inform health policy and practice and, thereby, support better decisions to promote long-term sustainability of healthcare systems. The healthcare utilisation of the Dutch type 2 diabetes population is distributed across the health system and utilisation of medical specialist care is high. This is likely to be due to presence of concurrent morbidities and complications. Therefore, a shift from a disease-specific approach to a person-centred and integrated care approach could be beneficial in the treatment of type 2 diabetes.

The Patient Centered Assessment Method (PCAM) for Action-Based Biopsychosocial Evaluation of Patient Needs: Validation and Perceived Value of the Dutch Translation.

The Patient Centered Assessment Method (PCAM) is an action-based tool that supports professionals to engage in a biopsychosocial assessment with patients and measure their needs. It is a promising tool for person-centered care. As the Netherlands lacks such a tool, a Dutch version was developed. Furthermore, we aimed to contribute to the relatively limited insights into the psychometric properties and value of the tool when used as part of a needs assessment in primary care. Confirmatory factor analysis was used to study construct validity and Cronbach's alpha was computed to assess reliability. Furthermore, we interviewed 15 primary care professionals who used the PCAM. It was confirmed that each PCAM domain measures a separate construct, informed by the biopsychosocial model. The tool showed adequate reliability (Cronbach's alpha = 0.83). Despite face validity concerns, the tool was mainly valued for measurement of patient needs and to facilitate action planning. Criticism of the PCAM pertained to a limited focus on the patient perspective, which is one of the crucial aspects of person-centered care. These rich, mixed-method insights can help to improve the value of the PCAM, as one of the few multifunctional tools to support professionals in holistic assessments.

Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification.

BACKGROUND: The Netherlands is currently investigating the feasibility of moving from fee-for-service to prospective payments for home healthcare, which would require a suitable case-mix system. In 2017, health insurers mandated a preliminary case-mix system as a first step towards generating information on client differences in relation to care use. Home healthcare providers have also increasingly adopted standardized nursing terminology (SNT) as part of their electronic health records (EHRs), providing novel data for predictive modelling. OBJECTIVE: To explore the predictive potential of SNT data for improvement of the existing preliminary Dutch case-mix classification for home healthcare utilization. METHODS: We extracted client-level data from the EHRs of a large home healthcare provider, including data from the existing Dutch case-mix system, SNT data (specifically, NANDA-I) and the hours of home healthcare provided. We evaluated the predictive accuracy of the case-mix system and the SNT data separately, and combined, using the machine learning algorithm Random Forest. RESULTS: The case-mix system had a predictive performance of 22.4% cross-validated R-squared and 6.2% cross-validated Cumming's Prediction Measure (CPM). Adding SNT data led to a substantial relative improvement in predicting home healthcare hours, yielding 32.1% R-squared and 15.4% CPM. DISCUSSION: The existing preliminary Dutch case-mix system distinguishes client needs to some degree, but not sufficiently. The results indicate that routinely collected SNT data contain sufficient additional predictive value to warrant further research for use in case-mix system design.

Person-centred and efficient care delivery for high-need, high-cost patients: primary care professionals' experiences.

BACKGROUND: High-need, high-cost (HNHC) patients, who typically have complex and long-term care demands, contribute considerably to the high work pressure of primary care professionals (PCPs). To improve patient as well as provider experiences, it is crucial to take into account the PCPs' perspective in designing health care strategies for HNHC patients. Therefore, this study aimed to create insight into PCPs' experienced barriers and possible solutions with regards to person-centred, efficient care delivery to HNHC patients. METHODS: We conducted a qualitative study using focus group interviews with PCPs at a Dutch primary care group. A semi-structured interview guide was developed for the interviews. Qualitative content analysis was employed deductively by means of a categorisation matrix. The matrix was based on the components retrieved from the SELFIE framework for integrated care for multi-morbidity. RESULTS: Forty-two PCPs participated in five focus group interviews. Discussed barriers and solutions were related to the core of the SELFIE framework (i.e. the individual and environment), and particularly four of the six health system components in the framework: service delivery, leadership & governance, workforce, and technologies & medical products. Many discussed barriers revolved around the complex biopsychosocial needs of HNHC patients: PCPs reported a lack of time (service delivery), insufficiently skilled PCPs (workforce), and inefficient patient information retrieval and sharing (technologies & medical products) as barriers to adequately meet the biopsychosocial needs of HNHC patients. CONCLUSIONS: This qualitative study suggests that primary care is currently insufficiently equipped to accommodate the complex biopsychosocial needs of HNHC patients. Therefore, it is firstly important to strengthen primary care internally, taking into account the experienced lack of time, the insufficient number of equipped PCPs and lack of inter-professional information retrieval and sharing. Secondly, PCPs should be supported in cooperating and communicating more efficiently with health services outside primary care to adequately deliver person-centred, efficient care. As a prerequisite, it is crucial to direct policy efforts at the design of a strong system of social and community services. In terms of future research, it is important to assess the feasibility and effects of re-designing primary care based on the provided recommendations.

A systematic review of case-mix models for home health care payment: Making sense of variation.

BACKGROUND: Case-mix based payment of health care services offers potential to contain expenditure growth and simultaneously support needs-based care provision. However, limited evidence exists on its application in home health care (HHC). Therefore, this study aimed to synthesize available international literature on existing case-mix models for HHC payment. METHODS: We performed a systematic review of scientific literature, supplemented with grey literature. We searched for literature using six scientific databases, reference lists, expert consultation, and targeted websites. Data on study design, case-mix model attributes, and conclusions were extracted narratively. RESULTS: Of 3303 references found, 22 scientific studies and 27 grey documents met eligibility criteria. Eight case-mix models for HHC were identified, from the US, Canada, New Zealand, Australia, and Germany. Three countries have implemented a case-mix model as part of a HHC payment system. Different combinations of in total 127 unique case-mix predictors are included across models to predict HHC use. Case-mix models also differ in targeted services, operationalization, and outcome measures and predictive power. CONCLUSIONS: Case-mix based payment is not yet widely used within HHC. Multiple varieties were found between HHC case-mix models, and no one best form of a model seems to exist. Even though varieties are partly inevitable due to country-specific contexts, developing a shared vision in case-mix model attributes would be key to achieving efficient, needs-based HHC.

Identifying subgroups of high-need, high-cost, chronically ill patients in primary care: A latent class analysis.

INTRODUCTION: Segmentation of the high-need, high-cost (HNHC) population is required for reorganizing care to accommodate person-centered, integrated care delivery. Therefore, we aimed to identify and characterize relevant subgroups of the HNHC population in primary care by using demographic, biomedical, and socioeconomic patient characteristics. METHODS: This was a retrospective cohort study within a Dutch primary care group, with a follow-up period from September 1, 2014 to August 31, 2017. Chronically ill patients were included in the HNHC population if they belonged to the top 10% of care utilizers and/or suffered from multimorbidity and had an above-average care utilization. In a latent class analysis, forty-one patient characteristics were initially used as potential indicators of heterogeneity in HNHC patients' needs. RESULTS: Patient data from 12 602 HNHC patients was used. A 4-class model was considered statistically and clinically superior. The classes were named according to the characteristics that were most dominantly present and distinctive between the classes (i.e. mainly age, household position, and source of income). Class 1 ('older adults living with partner') included 39.3% of patients, class 2 ('older adults living alone') included 25.5% of patients, class 3 ('middle-aged, employed adults with family') included 23.3% of patients, and class 4 ('middle-aged adults with social welfare dependency') included 11.9% of patients. Diabetes was the most common condition in all classes; the second most prevalent condition differed between osteoarthritis in class 1 (21.7%) and 2 (23.8%), asthma in class 3 (25.3%), and mood disorders in class 4 (23.1%). Furthermore, while general practitioner (GP) care utilization increased during the follow-up period in the classes of older adults, it remained relatively stable in the middle-aged classes. CONCLUSIONS: Although the HNHC population is heterogeneous, distinct subgroups with relatively homogeneous patterns of mainly demographic and socioeconomic characteristics can be identified. This calls for tailoring care and increased attention for social determinants of health.

Which client characteristics predict home-care needs? Results of a survey study among Dutch home-care nurses.

Fee-for-service, funding care on an hourly rate basis, creates an incentive for home-care providers to deliver high amounts of care. Under casemix funding, in contrast, clients are allocated-based on their characteristics-to homogenous, hierarchical groups, which are subsequently funded to promote more effective and efficient care. The first step in developing a casemix model is to understand which client characteristics are potential predictors of home-care needs. Nurses working in home care (i.e. home-care nurses) have a good insight into clients' home-care needs. This study was conducted in co-operation with the Dutch Nurses' Association and the Dutch Healthcare Authority. Based on international literature, 35 client characteristics were identified as potential predictors of home-care needs. In an online survey (May, 2017), Dutch home-care nurses were asked to score these characteristics on relevance, using a 9-point Likert scale. They were subsequently asked to identify the top five client characteristics. Data were analysed using descriptive statistics. The survey was completed by 1,007 home-care nurses. Consensus on relevance was achieved for 15 client characteristics, with "terminal phase" being scored most relevant, and "sex" being scored as the least relevant. Relevance of the remaining 20 characteristics was uncertain. Additionally, based on the ranking, "ADL functioning" was ranked as most relevant. According to home-care nurses, both biomedical and psychosocial client characteristics need to be taken into account when predicting home-care needs. Collaboration between clinical practice, policy development, and science is necessary to realise a funding model, to work towards the Triple Aim (improved health, better care experience, and lower costs).

The impact of redesigning care processes on quality of care: a systematic review.

BACKGROUND: This literature review evaluates the current state of knowledge about the impact of process redesign on the quality of healthcare. METHODS: Pubmed, CINAHL, Web of Science and Business Premier Source were searched for relevant studies published in the last ten years [2004-2014]. To be included, studies had to be original research, published in English with a before-and-after study design, and be focused on changes in healthcare processes and quality of care. Studies that met the inclusion criteria were independently assessed for excellence in reporting by three reviewers using the SQUIRE checklist. Data was extracted using a framework developed for this review. RESULTS: Reporting adequacy varied across the studies. Process redesign interventions were diverse, and none of the studies described their effects on all dimensions of quality defined by the Institute of Medicine. CONCLUSIONS: The results of this systematic literature review suggests that process redesign interventions have positive effects on certain aspects of quality. However, the full impact cannot be determined on the basis of the literature. A wide range of outcome measures were used, and research methods were limited. This review demonstrates the need for further investigation of the impact of redesign interventions on the quality of healthcare.

Estimating community health needs against a Triple Aim background: What can we learn from current predictive risk models?

INTRODUCTION: To support providers and commissioners in accurately assessing their local populations' health needs, this study produces an overview of Dutch predictive risk models for health care, focusing specifically on the type, combination and relevance of included determinants for achieving the Triple Aim (improved health, better care experience, and lower costs). METHODS: We conducted a mixed-methods study combining document analyses, interviews and a Delphi study. Predictive risk models were identified based on a web search and expert input. Participating in the study were Dutch experts in predictive risk modelling (interviews; n=11) and experts in healthcare delivery, insurance and/or funding methodology (Delphi panel; n=15). RESULTS: Ten predictive risk models were analysed, comprising 17 unique determinants. Twelve were considered relevant by experts for estimating community health needs. Although some compositional similarities were identified between models, the combination and operationalisation of determinants varied considerably. CONCLUSIONS: Existing predictive risk models provide a good starting point, but optimally balancing resources and targeting interventions on the community level will likely require a more holistic approach to health needs assessment. Development of additional determinants, such as measures of people's lifestyle and social network, may require policies pushing the integration of routine data from different (healthcare) sources.