Harvesting the wisdom of the crowd: using online ratings to explore care experiences in regions.

BACKGROUND: Regional population health management (PHM) initiatives need an understanding of regional patient experiences to improve their services. Websites that gather patient ratings have become common and could be a helpful tool in this effort. Therefore, this study explores whether unsolicited online ratings can provide insight into (differences in) patient's experiences at a (regional) population level. METHODS: Unsolicited online ratings from the Dutch website Zorgkaart Nederland (year = 2008-2017) were used. Patients rated their care providers on six dimensions from 1 to 10 and these ratings were geographically aggregated based on nine PHM regions. Distributions were explored between regions. Multilevel analyses per provider category, which produced Intraclass Correlation Coefficients (ICC), were performed to determine clustering of ratings of providers located within regions. If ratings were clustered, then this would indicate that differences found between regions could be attributed to regional characteristics (e.g. demographics or regional policy). RESULTS: In the nine regions, 70,889 ratings covering 4100 care providers were available. Overall, average regional scores (range = 8.3-8.6) showed significant albeit small differences. Multilevel analyses indicated little clustering between unsolicited provider ratings within regions, as the regional level ICCs were low (ICC pioneer site < 0.01). At the provider level, all ICCs were above 0.11, which showed that ratings were clustered. CONCLUSIONS: Unsolicited online provider-based ratings are able to discern (small) differences between regions, similar to solicited data. However, these differences could not be attributed to the regional level, making unsolicited ratings not useful for overall regional policy evaluations. At the provider level, ratings can be used by regions to identify under-performing providers within their regions.

Are low-value care measures up to the task? A systematic review of the literature.

BACKGROUND: Reducing low-value care is a core component of healthcare reforms in many Western countries. A comprehensive and sound set of low-value care measures is needed in order to monitor low-value care use in general and in provider-payer contracts. Our objective was to review the scientific literature on low-value care measurement, aiming to assess the scope and quality of current measures. METHODS: A systematic review was performed for the period 2010-2015. We assessed the scope of low-value care recommendations and measures by categorizing them according to the Classification of Health Care Functions. Additionally, we assessed the quality of the measures by 1) analysing their development process and the level of evidence underlying the measures, and 2) analysing the evidence regarding the validity of a selected subset of the measures. RESULTS: Our search yielded 292 potentially relevant articles. After screening, we selected 23 articles eligible for review. We obtained 115 low-value care measures, of which 87 were concentrated in the cure sector, 25 in prevention and 3 in long-term care. No measures were found in rehabilitative care and health promotion. We found 62 measures from articles that translated low-value care recommendations into measures, while 53 measures were previously developed by institutions as the National Quality Forum. Three measures were assigned the highest level of evidence, as they were underpinned by both guidelines and literature evidence. Our search yielded no information on coding/criterion validity and construct validity for the included measures. Despite this, most measures were already used in practice. CONCLUSION: This systematic review provides insight into the current state of low-value care measures. It shows that more attention is needed for the evidential underpinning and quality of these measures. Clear information about the level of evidence and validity helps to identify measures that truly represent low-value care and are sufficiently qualified to fulfil their aims through quality monitoring and in innovative payer-provider contracts. This will contribute to creating and maintaining the support of providers, payers, policy makers and citizens, who are all aiming to improve value in health care.

Measuring Population Health from a Broader Perspective: Assessing the My Quality of Life Questionnaire.

INTRODUCTION: Population health perspectives increasingly focus on people's perception of resilience, ability to adapt and self-manage. The goal of this study is to determine whether the MijnKwaliteitVanLeven.nl ("MyQualityOfLife.nl") survey is a valid and reliable instrument for assessing the broader health perspectives at population level. METHODS: 19,809 entries of the MyQualityOfLife.nl survey were used. To assess face validity, Huber's six dimensions of positive health were used as a framework for expert feedback. A confirmative factor analyses was done using the expert's item clustering, followed by data-driven explorative factor analyses and reliability tests. RESULTS: Experts distributed 74 of the 118 items over all six dimensions of positive health. The confirmatory factor analysis model based on expert classification was not confirmed. The subsequent exploratory factor analysis excluded most items based on factor loading and suggested two factors; 'quality of life' and 'daily functioning', both showing excellent reliability. CONCLUSION: The MyQualityOfLife.nl survey can assess the broader concept of health in a population as well as 'quality of life' and 'daily functioning'. However, the survey can currently not evaluate several of the positive health dimensions separately. Further research is needed to determine whether this is due to the instrument or the positive health dimensions.

How to Measure Population Health: An Exploration Toward an Integration of Valid and Reliable Instruments.

Population health management initiatives are introduced to transform health and community services by implementing interventions that combine various services and address the continuum of health and well-being of populations. Insight is required into a population's health to evaluate implementation of these initiatives. This study aims to determine the performance of commonly used instruments for measuring a population's experienced health and explores the assessed concepts of population health. Survey-based Short Form 12, version 2 (SF12, health status), Patient Activation Measure 13 (PAM13), and Kessler 10 (K10, psychological distress) data of 3120 respondents was used. Floor/ceiling effects were studied using descriptive statistics. Validity was assessed using factor and discriminant analyses, and reliability was assessed using Cronbach α. Finally, to study covered concepts, exploratory factor analyses (EFAs) were conducted, which included additional surveyed characteristics. The SF12 and PAM13 sum scores showed acceptable averages and distributions, while results of the K10 indicated a floor effect. SF12 and K10 measured their expected constructs, while PAM13 did not. The EFA of PAM13 displayed 1 instead of the expected 4 constructs. Reliability was good for all instruments (α 0.89-0.93). The overall EFA identified 4 concepts: mental, physical ability, lifestyle, and self-management. SF12 and PAM13, combined with lifestyle characteristics, are shown to provide insightful information to measure the physical, mental, lifestyle, and self-management concepts of population health. Future research should include additional instruments that cover new aspects introduced by recent definitions of health.

Comparing the Health of Populations: Methods to Evaluate and Tailor Population Management Initiatives in the Netherlands.

Health care no longer focuses solely on patients and increasingly emphasizes regions and their populations. Strategies, such as population management (PM) initiatives, aim to improve population health and well-being by redesigning health care and community services. Hence, insight into population health is needed to tailor interventions and evaluate their effects. This study aims to assess whether population health differs between initiatives and to what extent demographic, personal, and lifestyle factors affect these differences. A population health survey that included the Short Form 12 version 2 (SF12, physical and mental health status), Patient Activation Measure 13 (PAM13), and demographic, personal, and lifestyle factors was administered in 9 Dutch PM initiatives. Potential confounders were determined by comparing these factors between PM initiatives using analyses of variance and chi-square tests. The influence of these potential confounders on the health outcomes was studied using multivariate linear regression. Age, education, origin, employment, body mass index, and smoking were identified as potential confounders for differences found between the 9 PM initiatives. Each had a noteworthy influence on all of the instruments' scores. Not all health differences between PM initiatives were explained, as the SF12 outcomes still differed between PM initiatives once corrected. For the PAM13, the differences were no longer significant. Demographic and lifestyle factors should be included in the evaluation of PM initiatives and population health differences found can be used to tailor initiatives. Other factors beyond health care (eg, air quality) should be considered to further refine the tailoring and evaluation of PM initiatives.

Which Triple Aim related measures are being used to evaluate population management initiatives? An international comparative analysis.

INTRODUCTION: Population management (PM) initiatives are introduced in order to create sustainable health care systems. These initiatives should focus on the continuum of health and well-being of a population by introducing interventions that integrate various services. To be successful they should pursue the Triple Aim, i.e. simultaneously improve population health and quality of care while reducing costs per capita. This study explores how PM initiatives measure the Triple Aim in practice. METHOD: An exploratory search was combined with expert consultations to identify relevant PM initiatives. These were analyzed based on general characteristics, utilized measures and related selection criteria. RESULTS: In total 865 measures were used by 20 PM initiatives. All quality of care domains were included by at least 11 PM initiatives, while most domains of population health and costs were included by less than 7 PM initiatives. Although their goals showed substantial overlap, the measures applied showed few similarities between PM initiatives and were predominantly selected based on local priority areas and data availability. CONCLUSION: Most PM initiatives do not measure the full scope of the Triple Aim. Additionally, variety between measures limits comparability between PM initiatives. Consensus on the coverage of Triple Aim domains and a set of standardized measures could further both the inclusion of the various domains as well as the comparability between PM initiatives.