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The Patient-Reported Outcomes Measurement Information System (PROMIS) is a universally applicable set of instruments, including item banks, short forms and computer adaptive tests (CATs), measuring patient-reported health across different patient populations. PROMIS CATs are highly efficient and the use in practice is considered feasible with little administration time, offering standardized and routine patient monitoring. Before an item bank can be used as CAT, the psychometric properties of the item bank have to be examined. Therefore, the objective was to assess the psychometric properties of the Dutch-Flemish PROMIS Physical Function item bank (DF-PROMIS-PF) in Dutch patients receiving physical therapy. Cross-sectional study. 805 patients >18 years, who received any kind of physical therapy in primary care in the past year, completed the full DF-PROMIS-PF (121 items). Unidimensionality was examined by Confirmatory Factor Analysis and local dependence and monotonicity were evaluated. A Graded Response Model was fitted. Construct validity was examined with correlations between DF-PROMIS-PF T-scores and scores on two legacy instruments (SF-36 Health Survey Physical Functioning scale [SF36-PF10] and the Health Assessment Questionnaire Disability-Index [HAQ-DI]). Reliability (standard errors of theta) was assessed. The results for unidimensionality were mixed (scaled CFI = 0.924, TLI = 0.923, RMSEA = 0.045, 1th factor explained 61.5% of variance). Some local dependence was found (8.2% of item pairs). The item bank showed a broad coverage of the physical function construct (threshold-parameters range: -4.28-2.33) and good construct validity (correlation with SF36-PF10 = 0.84 and HAQ-DI = -0.85). Furthermore, the DF-PROMIS-PF showed greater reliability over a broader score-range than the SF36-PF10 and HAQ-DI. The psychometric properties of the DF-PROMIS-PF item bank are sufficient. The DF-PROMIS-PF can now be used as short forms or CAT to measure the level of physical function of physiotherapy patients.

Background: Around the world, policies and interventions are used to encourage clinicians to reduce low-value care. In order to facilitate this, we need a better understanding of the factors that lead to low-value care. We aimed to identify the key factors affecting low-value care on a national level. In addition, we highlight differences and similarities in three countries. Methods: We performed 18 semi-structured interviews with experts on low-value care from three countries that are actively reducing low-value care: the United States, Canada, and the Netherlands. We interviewed 5 experts from Canada, 6 from the United States, and 7 from the Netherlands. Eight were organizational leaders or policy-makers, 6 as low-value care researchers or project leaders, and 4 were both. The transcribed interviews were analyzed using inductive thematic analysis. Results: The key factors that promote low-value care are the payment system, the pharmaceutical and medical device industry, fear of malpractice litigation, biased evidence and knowledge, medical education, and a ‘more is better’ culture. These factors are seen as the most important in the United States, Canada and the Netherlands, although there are several differences between these countries in their payment structure, and industry and malpractice policy. Conclusion: Policy-makers and researchers that aim to reduce low-value care have experienced that clinicians face a mix of interdependent factors regarding the healthcare system and culture that lead them to provide low-value care. Better awareness and understanding of these factors can help policy-makers to facilitate clinicians and medical centers to deliver high-value care.

Objective Noninvasive telemonitoring aims to improve healthcare for patients with chronic heart failure (HF) by reducing hospitalizations and improving patient experiences. Yet, sustainable adoption seems to be limited. Therefore, the goal of our study is to gain insight in the processes that support sustainable adoption of telemonitoring for patients with HF. Methods We conducted semi-structured interviews with 25 stakeholders that were involved with the adoption of telemonitoring, such as healthcare professionals, policymakers and healthcare insurers. We analyzed the interviews by using a combination of open-coding and the themes of the Non-adoption or Abandonment of technology by individuals and difficulties achieving Scale-up, Spread and Sustainability framework. Results We found that telemonitoring projects have moved beyond initial pilot phases despite a high level of complexity on multiple topics. The patient selection, the business case, the evidence, the aims of telemonitoring, integration of telemonitoring in the care pathway, reimbursement, and future centralization were items that yielded different and sometimes contradictory opinions. Conclusions This study showed that the sustainable adoption of telemonitoring for HF is a complex endeavor. Different aims and perspectives play an important role in the patient selection, design, evaluations and envisioned futures of telemonitoring. High conviction among participants of the added value that telemonitoring may support further adoption of telemonitoring. Structural evaluations will be needed to guide cyclical improvement and adapt programs to employ telemonitoring in such a manner that it contributes to collectively supported aims.

Chronic heart failure (HF) is a chronic disease affecting more than 64 million people worldwide, with an increasing prevalence and a high burden on individual patients and society. Telemonitoring may be able to mitigate some of this burden by increasing self-management and preventing use of the health care system. However, it is unknown to what degree telemonitoring has been adopted by hospitals and if the use of telemonitoring is associated with certain patient characteristics. Insight into the dissemination of this technology among hospitals and patients may inform strategies for further adoption. We aimed to explore the use of telemonitoring among hospitals in the Netherlands and to identify patient characteristics associated with the use of telemonitoring for HF. We performed a retrospective cohort study based on routinely collected health care claim data in the Netherlands. Descriptive analyses were used to gain insight in the adoption of telemonitoring for HF among hospitals in 2019. We used logistic multiple regression analyses to explore the associations between patient characteristics and telemonitoring use. Less than half (31/84, 37%) of all included hospitals had claims for telemonitoring, and 20% (17/84) of hospitals had more than 10 patients with telemonitoring claims. Within these 17 hospitals, a total of 7040 patients were treated for HF in 2019, of whom 5.8% (409/7040) incurred a telemonitoring claim. Odds ratios (ORs) for using telemonitoring were higher for male patients (adjusted OR 1.90, 95% CI 1.50-2.41) and patients with previous hospital treatment for HF (adjusted OR 1.76, 95% CI 1.39-2.24). ORs were lower for higher age categories and were lowest for the highest age category, that is, patients older than 80 years (OR 0.30, 95% CI 0.21-0.44) compared to the reference age category (18-59 years). Socioeconomic status, degree of multimorbidity, and excessive polypharmacy were not associated with the use of telemonitoring. The use of reimbursed telemonitoring for HF was limited up to 2019, and our results suggest that large variation exists among hospitals. A lack of adoption is therefore not only due to a lack of diffusion among hospitals but also due to a lack of scaling up within hospitals that already deploy telemonitoring. Future studies should therefore focus on both kinds of adoption and how to facilitate these processes. Older patients, female patients, and patients with no previous hospital treatment for HF were less likely to use telemonitoring for HF. This shows that some patient groups are not served as much by telemonitoring as other patient groups. The underlying mechanism of the reported associations should be identified in order to gain a deeper understanding of telemonitoring use among different patient groups.

Chronic heart failure accounts for approximately 1%-2% of health care expenditures in most developed countries. These costs are primarily driven by hospitalizations and comorbidities. Telemonitoring has been proposed to reduce the number of hospitalizations and decrease the cost of treatment for patients with heart failure. However, the effects of telemonitoring on health care utilization remain unclear. This systematic review aims to study the effect of telemonitoring programs on health care utilization and costs in patients with chronic heart failure. We assess the effect of telemonitoring on hospitalizations, emergency department visits, length of stay, hospital days, nonemergency department visits, and health care costs. We searched PubMed, Embase, and Web of Science for randomized controlled trials and nonrandomized studies on noninvasive telemonitoring and health care utilization. We included studies published between January 2010 and August 2020. For each study, we extracted the reported data on the effect of telemonitoring on health care utilization. We used P<.05 and CIs not including 1.00 to determine whether the effect was statistically significant. We included 16 randomized controlled trials and 13 nonrandomized studies. Inclusion criteria, population characteristics, and outcome measures differed among the included studies. Most studies showed no effect of telemonitoring on health care utilization. The number of hospitalizations was significantly reduced in 38% (9/24) of studies, whereas emergency department visits were reduced in 13% (1/8) of studies. An increase in nonemergency department visits (6/9, 67% of studies) was reported. Health care costs showed ambiguous results, with 3 studies reporting an increase in health care costs, 3 studies reporting a reduction, and 4 studies reporting no significant differences. Health care cost reductions were realized through a reduction in hospitalizations, whereas increases were caused by the high costs of the telemonitoring program or increased health care utilization. Most telemonitoring programs do not show clear effects on health care utilization measures, except for an increase in nonemergency outpatient department visits. This may be an unwarranted side effect rather than a prerequisite for effective telemonitoring. The consequences of telemonitoring on nonemergency outpatient visits should receive more attention from regulators, payers, and providers. This review further demonstrates the high clinical and methodological heterogeneity of telemonitoring programs. This should be taken into account in future meta-analyses aimed at identifying the effective components of telemonitoring programs.

A national clinical registry was established in the Netherlands containing data directly sampled from electronic health record systems of physical therapists (PTs). This registry aims to evaluate the potential of patient reported outcome measures (PROMs) to develop quality indicators (QIs) in physical therapy care. To test to what extent the collected PROM data are reliable, valid and discriminatory between practices in measuring outcomes of patients with non-specific low back pain (NSLBP). In this retrospective cohort study 865 PT practices with 6,560 PTs voluntarily collected PROM data of patients with NSLBP, using the Quebec Back Pain Disability Scale (QBPDS), the Numeric Pain Rating Scale (NPRS) and the Patient Specific Functioning Scale (PSFS). Reliability was determined by analysing the completeness of the dataset, the comparability by using national reference data, and through checking selection bias in the included patients. Validity was tested using the known-groups contrast between patients with (sub)acute vs. chronic NSLBP. To determine discriminative ability of outcomes between PT practices, case-mix corrected hierarchical multilevel analyses were performed. Reliability was sufficient by confirming fifteen of the sixteen hypotheses: 59% of all patients opted in for data analysis, 42% of these included patients showed repeated measurement, comparing with reference data and potential selection bias showed < 5% between group differences, while differences between (sub)acute and chronic NSLB-groups were significantly larger than 5% (less treatment sessions, lager differences in outcomes in (sub)acute NSLB patients). In addition, all nine adjusted hierarchical multilevel models confirm that the collected dataset on outcomes in PT care is able to discriminate between practices using PROM results of patients with NSLBP (ICC-scores range 0.11-0.21). PROMs of patients with NSLBP collected in the national clinical registry of KNGF are reliable, valid and able to discriminate between primary care PT practices.

Background The Netherlands has reformed its long-term care (LTC) system to improve its financial sustainability by reducing access to in-kind nursing-home care and stimulating care within the home setting. This reform led to the rise of small-scale homelike nursing homes (SHNH) that are publicly financed by home-care packages. Small-scale care may require additional financial resources and personnel. Our aim is to compare the total healthcare costs of institutional in-kind nursing homes and deinstitutionalized home-like care homes. Methods We conducted an instrumental variable (IV) analysis to adjust for selection effects between both groups, using nationwide data including all LTC users. We use distance to the nearest provider as an instrument. In addition, we conducted semi-structured interviews with managers and policymakers to provide context for the quantitative data and validate our findings. Results Baseline statistics demonstrate that while LTC costs are lower in deinstitutionalized settings, total healthcare costs are higher, mainly due to additional medical costs outside the LTC system. After adjustment for observable and unobservable user characteristics, no significant cost differences were found. Conclusions This study may suggest that deinstitutionalization of LTC provision through publicly funded SNHNs primarily resulted in selection effects and spillover effects to other healthcare sectors, while for the marginal client, no clear difference in healthcare costs was observed.

ObjectiveWe aimed to increase the understanding of the scaling of de-implementation strategies by identifying the determinants of the process and developing a determinant framework.Design and methodsThis study has a mixed-methods design. First, we performed an integrative review to build a literature-based framework describing the determinants of the scaling of healthcare innovations and interventions. PubMed and EMBASE were searched for relevant studies from 1995 to December 2020. We systematically extracted the determinants of the scaling of interventions and developed a literature-based framework. Subsequently, this framework was discussed in four focus groups with national and international de-implementation experts. The literature-based framework was complemented by the findings of the focus group meetings and adapted for the scaling of de-implementation strategies.ResultsThe literature search resulted in 42 articles that discussed the determinants of the scaling of innovations and interventions. No articles described determinants specifically for de-implementation strategies. During the focus groups, all participants agreed on the relevance of the extracted determinants for the scaling of de-implementation strategies. The experts emphasised that while the determinants are relevant for various countries, the implications differ due to different contexts, cultures and histories. The analyses of the focus groups resulted in additional topics and determinants, namely, medical training, professional networks, interests of stakeholders, clinical guidelines and patients’ perspectives. The results of the focus group meetings were combined with the literature framework, which together formed the supporting the scaling of de-implementation strategies (SPREAD) framework. The SPREAD framework includes determinants from four domains: (1) scaling plan, (2) external context, (3) de-implementation strategy and (4) adopters.ConclusionsThe SPREAD framework describes the determinants of the scaling of de-implementation strategies. These determinants are potential targets for various parties to facilitate the scaling of de-implementation strategies. Future research should validate these determinants of the scaling of de-implementation strategies.

Background Adequate staffing and manageable workloads are crucial for high-quality emergency care. However, high perceived workloads in the emergency department (ED) threaten both. Increased demand and staff shortages intensify these issues and cause crowding. Understanding healthcare workers’ perspectives on workload is essential for effective policymaking and maintaining a durable workforce. The objective of this study was to explore the current perceived workload of ED professionals, and their vision on how to influence it and to understand the healthcare worker’s perspective on proposed healthcare reforms in the Netherlands. Methods Qualitive study using semi-structured interviews with 33 healthcare professionals (ED physicians, physician assistants and nurses) working in a university medical center, and three general hospitals from March-July 2023. The interviews were audio-recorded and transcribed verbatim. The interview transcripts were coded. An inductive content analysis was performed, where codes were assigned to the transcripts and adjusted. Codes were then sorted into themes on causes of workload, consequences and proposed solutions for reducing the workload. Results Emergency department professionals identified increasing patient inflow, barriers to patient throughput and output, and staff shortages as key workload causes. Consequences included increased time, mental and psychological demands, leading to stress and reduced job satisfaction. A well-coordinated team and having social interaction with colleagues is seen as a key part of the profession by ED-professionals. Next to that, training and development opportunities contribute to job satisfaction. Proposed solutions involved public education, improved care coordination, technological innovations, and enhanced collaboration within and among hospitals to improve efficiency and job satisfaction, and to reduce perceived workload pressure. Conclusions Addressing perceived workload is crucial for maintaining quality and accessibility of care. Dutch ED professionals face a high workload leading to stress and lower job satisfaction. To ensure successful changes, adequate preconditions and engaging healthcare professionals in decision-making are crucial. In future research, a stronger focus on effective work habits and processes, including provider satisfaction and workplace well-being, is necessary.

Background Patient reported outcomes (PROs) provide information on a patient’s health status coming directly from the patient. Measuring PROs with patient reported outcome measures (PROMs) has gained wide interest in clinical practice for individual patient care, as well as in quality improvement, and for providing transparency of outcomes to stakeholders through public reporting. However, current knowledge of selecting and implementing PROMs for these purposes is scattered, and not readily available for clinicians and quality managers in healthcare organizations. The objective of this study is to develop a framework with tools to support the systematic selection, implementation and evaluation of PROs and PROMs in individual patient care, for quality improvement and public reporting. Methods We developed the framework in a national project in the Netherlands following a user-centered design. The development process of the framework contained five iterative components: (a) identification of existing tools, (b) identification of user requirements and designing steps for selection and implementation of PROs and PROMs, (c) discussing a prototype of the framework during a national workshop, (d) developing a web version, (e) pre-testing of the framework. A total of 40 users with different perspectives (clinicians, patient representatives, quality managers, purchasers, researchers) have been consulted. Results The final framework is presented as the PROM-cycle that consists of eight steps in four phases: (1) goal setting, (2) selecting PROs and PROMs, (3) developing and testing of quality indicator(s), (4) implementing and evaluating the PROM(s) and indicator(s). Users emphasized that the first step is the key element in which the why, for whom and setting of the PROM has to be defined. This information is decisive for the following steps. For each step the PROM-cycle provides guidance and tools, with instruments, checklists, methods, handbooks, and standards supporting the process. Conclusion We developed a framework to support the selection and implementation of PROs and PROMs. Each step provides guidance and tools to support the process. The PROM-cycle and its tools are publicly available and can be used by clinicians, quality managers, patient representatives and other experts involved in using PROMS. Through periodic evaluation and updates, tools will be added for national and international use of the PROM-cycle.