[Pursuing the triple aim: evaluation of the integrated care system Gesundes Kinzigtal: population health, patient experience and cost-effectiveness]. / Triple Aim - Evaluation in der Integrierten Versorgung Gesundes Kinzigtal - Gesundheitszustand, Versorgungserleben und Wirtschaftlichkeit.

BACKGROUND: The integrated care system Gesundes Kinzigtal (ICSGK), one of the most comprehensive population-based ICS in Germany, started its work nearly 9 years ago. The ICSGK is pursuing the Triple Aim: improving the health of the population, improving the individual's experience of care, and at the same time reducing the per capita costs of care. OBJECTIVES: To evaluate the impact of the ICSGK on the Triple Aim. MATERIALS AND METHODS: The ICSGK is being evaluated externally and internally via a mix of diverse quantitative and qualitative methods. This paper presents selected results for each Triple Aim dimension. RESULTS AND CONCLUSIONS: Regarding population health, most of the quality indicators examined by the external scientific evaluation show positive development. For example, the prevalence of patients with fractures among all insurants with osteoporosis is presented. In 2011, this prevalence was approximately 26 % in the "Kinzigtal" population (aged ≥ 20 years old) in comparison to 33 % in the control group. As far as patient experience is concerned, to the question "Would you recommend becoming a member of Gesundes Kinzigtal to your friends or relatives?" 92.1 % of those questioned answered "Yes, for sure" or "Yes, probably." Twenty-four percent of those questioned further stated that they would now live "more healthy" than before enrolment in the ICSGK. In the subgroup of questioned insurants who had objective agreements with their doctors 45.4 % answered in this way. On the subject of cost-effectiveness, for both participating socil health insurance schemes, cost savings relative to the costs normally expected for the ICSGK population concerned are observed every year. In the seventh intervention year (2012) the total is 4.56 million Euros for the AOK Baden-Württemberg (BW), which is a contribution margin of 146 Euros per insurant for the 31.156 insurants concerned (LKK BW = 322 Euros per insurant relative to cost savings). The results presented in this paper indicate positive effects in all three Triple Aim dimensions. Further longitudinal studies are recommended to validate those first results together with a detailed analysis to obtain in-depth insights into the specific influence of subcomponents of the total intervention.

Reactive oxygen species-activated Ca/calmodulin kinase IIδ is required for late I(Na) augmentation leading to cellular Na and Ca overload.

RATIONALE: In heart failure Ca/calmodulin kinase (CaMK)II expression and reactive oxygen species (ROS) are increased. Both ROS and CaMKII can increase late I(Na) leading to intracellular Na accumulation and arrhythmias. It has been shown that ROS can activate CaMKII via oxidation. OBJECTIVE: We tested whether CaMKIIδ is required for ROS-dependent late I(Na) regulation and whether ROS-induced Ca released from the sarcoplasmic reticulum (SR) is involved. METHODS AND RESULTS: 40 µmol/L H(2)O(2) significantly increased CaMKII oxidation and autophosphorylation in permeabilized rabbit cardiomyocytes. Without free [Ca](i) (5 mmol/L BAPTA/1 mmol/L Br(2)-BAPTA) or after SR depletion (caffeine 10 mmol/L, thapsigargin 5 µmol/L), the H(2)O(2)-dependent CaMKII oxidation and autophosphorylation was abolished. H(2)O(2) significantly increased SR Ca spark frequency (confocal microscopy) but reduced SR Ca load. In wild-type (WT) mouse myocytes, H(2)O(2) increased late I(Na) (whole cell patch-clamp). This increase was abolished in CaMKIIδ(-/-) myocytes. H(2)O(2)-induced [Na](i) and [Ca](i) accumulation (SBFI [sodium-binding benzofuran isophthalate] and Indo-1 epifluorescence) was significantly slowed in CaMKIIδ(-/-) myocytes (versus WT). CaMKIIδ(-/-) myocytes developed significantly less H(2)O(2)-induced arrhythmias and were more resistant to hypercontracture. Opposite results (increased late I(Na), [Na](i) and [Ca](i) accumulation) were obtained by overexpression of CaMKIIδ in rabbit myocytes (adenoviral gene transfer) reversible with CaMKII inhibition (10 µmol/L KN93 or 0.1 µmol/L AIP [autocamtide 2-related inhibitory peptide]). CONCLUSIONS: Free [Ca](i) and a functional SR are required for ROS activation of CaMKII. ROS-activated CaMKIIδ enhances late I(Na), which may lead to cellular Na and Ca overload. This may be of relevance in hear failure, where enhanced ROS production meets increased CaMKII expression.

The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data.

Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high-cost patients in the following year. Therefore, we exploited routinely collected sickness funds claims and cost data of the years 2016, 2017 and 2018. Various specifications of each algorithm were trained and cross-validated on training data (n = 20,984) with claims and cost data from 2016 and outcomes from 2017. The best performing specifications of each algorithm were selected based on validation dataset performance. For performance comparison, selected models were applied to unforeseen data with features of the year 2017 and outcomes of the year 2018 (n = 21,146). The RF was the best performing algorithm measured by the area under the receiver operating curve (AUC) with a value of 0.883 (95% confidence interval (CI): 0.872-0.893) on test data, followed by the GBM (AUC = 0.878; 95% CI: 0.867-0.889). The ANN (AUC = 0.846; 95% CI: 0.834-0.857) and LR (AUC = 0.839; 95% CI: 0.826-0.852) were significantly outperformed by the GBM and the RF. All ML algorithms and the LR performed ´good´ (i.e. 0.9 > AUC ≥ 0.8). We were able to develop machine learning models that predict high-cost patients with 'good' performance facilitating routinely collected sickness fund claims and cost data. We found that tree-based models performed best and outperformed the ANN and LR.

Big Data Analytics to Reduce Preventable Hospitalizations-Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions.

The purpose of this study was to develop a prediction model to identify individuals and populations with a high risk of being hospitalized due to an ambulatory care-sensitive condition who might benefit from preventative actions or tailored treatment options to avoid subsequent hospital admission. A rate of 4.8% of all individuals observed had an ambulatory care-sensitive hospitalization in 2019 and 6389.3 hospital cases per 100,000 individuals could be observed. Based on real-world claims data, the predictive performance was compared between a machine learning model (Random Forest) and a statistical logistic regression model. One result was that both models achieve a generally comparable performance with c-values above 0.75, whereas the Random Forest model reached slightly higher c-values. The prediction models developed in this study reached c-values comparable to existing study results of prediction models for (avoidable) hospitalization from the literature. The prediction models were designed in such a way that they can support integrated care or public and population health interventions with little effort with an additional risk assessment tool in the case of availability of claims data. For the regions analyzed, the logistic regression revealed that switching to a higher age class or to a higher level of long-term care and unit from prior hospitalizations (all-cause and due to an ambulatory care-sensitive condition) increases the odds of having an ambulatory care-sensitive hospitalization in the upcoming year. This is also true for patients with prior diagnoses from the diagnosis groups of maternal disorders related to pregnancy, mental disorders due to alcohol/opioids, alcoholic liver disease and certain diseases of the circulatory system. Further model refinement activities and the integration of additional data, such as behavioral, social or environmental data would improve both model performance and the individual risk scores. The implementation of risk scores identifying populations potentially benefitting from public health and population health activities would be the next step to enable an evaluation of whether ambulatory care-sensitive hospitalizations can be prevented.

Inhibition of elevated Ca2+/calmodulin-dependent protein kinase II improves contractility in human failing myocardium.

RATIONALE: Heart failure (HF) is known to be associated with increased Ca(2+)/calmodulin-dependent protein kinase (CaMK)II expression and activity. There is still controversial discussion about the functional role of CaMKII in HF. Moreover, CaMKII inhibition has never been investigated in human myocardium. OBJECTIVE: We sought to investigate detailed CaMKIIδ expression in end-stage failing human hearts (dilated and ischemic cardiomyopathy) and the functional effects of CaMKII inhibition on contractility. METHODS AND RESULTS: Expression analysis revealed that CaMKIIδ, both cytosolic δ(C) and nuclear δ(B) splice variants, were significantly increased in both right and left ventricles from patients with dilated or ischemic cardiomyopathy versus nonfailing. Experiments with isometrically twitching trabeculae revealed significantly improved force frequency relationships in the presence of CaMKII inhibitors (KN-93 and AIP). Increased postrest twitches after CaMKII inhibition indicated an improved sarcoplasmic reticulum (SR) Ca(2+) loading. This was confirmed in isolated myocytes by a reduced SR Ca(2+) spark frequency and hence SR Ca(2+) leak, resulting in increased SR Ca(2+) load when inhibiting CaMKII. Ryanodine receptor type 2 phosphorylation at Ser2815, which is known to be phosphorylated by CaMKII thereby contributing to SR Ca(2+) leak, was found to be markedly reduced in KN-93-treated trabeculae. Interestingly, CaMKII inhibition did not influence contractility in nonfailing sheep trabeculae. CONCLUSIONS: The present study shows for the first time that CaMKII inhibition acutely improves contractility in human HF where CaMKIIδ expression is increased. The mechanism proposed consists of a reduced SR Ca(2+) leak and consequently increased SR Ca(2+) load. Thus, CaMKII inhibition appears to be a possible therapeutic option for patients with HF and merits further investigation.

How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review.

Introduction: Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is "How can big data analytics support people-centred and integrated health services?" Methods: A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review. Results: Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%). Discussion: The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics. Conclusions: Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.

Nursing home-sensitive conditions: analysis of routine health insurance data and modified Delphi analysis of potentially avoidable hospitalizations.

Background: Hospitalizations of nursing home residents are associated with various health risks. Previous research indicates that, to some extent, hospitalizations of this vulnerable population may be inappropriate and even avoidable. This study aimed to develop a consensus list of hospital discharge diagnoses considered to be nursing home-sensitive, i.e., avoidable. Methods: The study combined analyses of routine data from six statutory health insurance companies in Germany and a two-stage Delphi panel, enhanced by expert workshop discussions, to identify and corroborate relevant diagnoses. Experts from four different disciplines estimated the proportion of hospitalizations that could potentially have been prevented under optimal conditions.  Results: We analyzed frequencies and costs of data for hospital admissions from 242,236 nursing home residents provided by statutory health insurance companies. We identified 117 hospital discharge diagnoses, which had a frequency of at least 0.1%. We recruited experts (primary care physicians, hospital specialists, nursing home professionals and researchers) to estimate the proportion of potentially avoidable hospitalizations for the 117 diagnoses deemed avoidable in two Delphi rounds (n=107 in Delphi Round 1 and n=96 in Delphi Round 2, effective response rate=91%). A total of 35 diagnoses with high and consistent estimates of the proportion of potentially avoidable hospitalizations were identified as nursing home-sensitive. In an expert workshop (n=16), a further 25 diagnoses were discussed that had not reached the criteria, of which another 23 were consented to be nursing home-sensitive conditions. Extrapolating the frequency and mean costs of these 58 diagnoses to the national German context yielded total potentially avoidable care costs of €768,304,547, associated with 219,955 nursing home-sensitive hospital admissions. Conclusion: A total of 58 nursing home-relevant diagnoses (ICD-10-GM three-digit level) were classified as nursing home-sensitive using an adapted Delphi procedure. Interventions should be developed to avoid hospital admission from nursing homes for these diagnoses.

[High-cost patients in Germany: General description of utilization and costs]. / Hochkostenversicherte in Deutschland: Leistungs- und Kostenprofile.

BACKGROUND: Studies from different countries have shown that a small number of insured persons (high-cost patients) are responsible for a large portion of health care spending. At the same time, it is assumed that some of these costs could be saved by a better management of this group of people. The aim of this article is to analyze the performance and cost profiles of high-cost patients, to put them in an international comparison, and to derive a better management approach. METHODS: Retrospective observation study based on statutory health insurance data from two statutory health insurances for the year 2013. STUDY POPULATION: top 5 %, as well as top 1 % of the most expensive insured persons. Identification of characteristics of high-cost patients and international comparison with the Netherlands, the USA, Canada, Spain, England and Japan. RESULTS: 5 % of insured persons account for almost half of the total costs and the most expensive 1 % of 22 %. These high-cost patients in Germany are, on average, 20 years older than the general population. Almost every person of the high-cost population was prescribed at least one medication during the study period (99.2 %), and 85.8 % had at least one hospital stay. Hospital care accounts for the biggest part of total costs: 75 % together with drugs. The average per capita costs caused by one of the 5 % most expensive insured persons in the year under review are 20 times higher than that of the other 95 % of insured persons. High-cost patients are generally more multimorbid and have higher mortality rates. The most common diagnoses of these patients are hypertension, lipoprotein metabolism disorder and back pain. CONCLUSION: Similar to other developed countries, Germany faces the challenge to develop and implement adequate intervention approaches addressing the special requirements of high-cost insured persons. This paper provides a first basis. The analogies of high-cost patients in Germany and other countries illustrate the need for transnational research and intervention approaches on this specific issue. More in-depth work is needed to investigate the potentials of Predictive Modelling and integrated care approaches to the management of this group of insured persons.

The FORTA (Fit fOR The Aged)-EPI (Epidemiological) Algorithm: Application of an Information Technology Tool for the Epidemiological Assessment of Drug Treatment in Older People.

BACKGROUND: To improve drug treatment in older people, who often present with multimorbidity and related polypharmacy, the FORTA (Fit fOR The Aged) List was developed via a Delphi consensus procedure. As a patient-in-focus listing approach (PILA), it has been clinically validated (VALFORTA trial). Unlike drug-oriented listing approaches (DOLAs), its application requires knowledge of patients' characteristics, including diagnoses and other details. As a drug list with discrete labels, application of FORTA seems particularly amenable to electronic support. METHODS: An information technology (IT) algorithm was developed to analyze bulk data on International Classification of Diseases (ICD)-coded diseases and Anatomical Therapeutic Chemical (ATC)-coded drugs. FORTA-labeled diagnoses and drugs were used to compute the FORTA score, an automatically generated score that describes medication quality by adding up points assigned for errors related to over- and under-treatment. The algorithm detects mismatches between diagnoses and drugs, suboptimal drugs, omitted drugs, and deficient medication escalation schemes. The read-out produces explanations for each error point. RESULTS: A total of 5603 and 7954 patients ≥ 65 years were included from two claims datasets (> 30,000 patients each, public health insurance). The FORTA scores were comparable (mean ± standard deviation 4.29 ± 3.37 vs. 4.17 ± 3.16), and similar to that determined in VALFORTA (pre-intervention 3.5 ± 2.7). Under-treatment was two times more prevalent than over-treatment. The main areas of under-treatment were pain, type 2 diabetes mellitus, and depression, and the main areas of over-treatment were gastrointestinal (proton pump inhibitors), pain (non-steroidal anti-inflammatory drugs), and arterial hypertension (ß-blockers). The FORTA score is positively correlated with higher age, a higher Charlson Comorbidity Index, and more frequent hospitalizations. Patients in disease management programs run by public health insurers had higher scores than comparators. CONCLUSIONS: The algorithm produces plausible analyses of medication errors in older people, pointing to established areas of therapeutic deficiencies. Though individual recommendations exist, the algorithm cannot employ the full potential of FORTA as important details (e.g., blood pressure values, pain intensity) are not (yet) included. However, it seems capable of detecting medication problems in large cohorts-FORTA-EPI (Epidemiological) is designed to support epidemiological analyses, e.g., on comparisons of large cohorts, interventional impact, or longitudinal trends.

Evaluating the Impact of an Accountable Care Organization on Population Health: The Quasi-Experimental Design of the German Gesundes Kinzigtal.

A central goal of accountable care organizations (ACOs) is to improve the health of their accountable population. No evidence currently links ACO development to improved population health. A major challenge to establishing the evidence base for the impact of ACOs on population health is the absence of a theoretically grounded, robust, operationally feasible, and meaningful research design. The authors present an evaluation study design, provide an empirical example, and discuss considerations for generating the evidence base for ACO implementation. A quasi-experimental study design using propensity score matching in combination with small-scale exact matching is implemented. Outcome indicators based on claims data were constructed and analyzed. Population health is measured by using a range of mortality indicators: mortality ratio, age at time of death, years of potential life lost/gained, and survival time. The application is assessed using longitudinal data from Gesundes Kinzigtal, one of the leading population-based ACOs in Germany. The proposed matching approach resulted in a balanced control of observable differences between the intervention (ACO) and control groups. The mortality indicators used indicate positive results. For example, 635.6 fewer years of potential life lost (2005.8 vs. 2641.4; t-test: sig. P < 0.05*) in the ACO intervention group (n = 5411) attributable to the ACO, also after controlling for a potential (indirect) immortal time bias by excluding the first half year after enrollment from the outcome measurement. This empirical example of the impact of a German ACO on population health can be extended to the evaluation of ACOs and other integrated delivery models of care.

[A pilot study to evaluate the DMP for coronary heart disease - Development of a methodology and first results]. / Pilotstudie zur Evaluation des DMP Koronare Herzkrankheit - Entwicklung einer Methodik und erste Ergebnisse.

Regarding the effectiveness of disease management programs (DMPs) in Germany, several studies have been published on the DMP for type 2 diabetes. This pilot study provides methodological insights into evaluating the DMP for coronary heart disease (CHD), which currently includes 1.7 million participants, and reveals trends in healthcare outcomes for mortality, guideline adherent prescribing and costs. Major methodological challenges that need to be considered for the development of an appropriate matching method for this indication have been identified. The results show positive trends in favor of the DMP regarding mortality, costs and medication according to guidelines. A matching design is applicable to the CHD indication; the knowledge gained regarding the quality of care can be used for a targeted development of the program.