Toward the Measure of Credibility of Hospital Administrative Datasets in the Context of DRG Classification

Poor quality of coded clinical data in hospital administrative databases may negatively affect decision making, clinical and health care services research and billing. In this paper, we assessed the level of credibility of a nationwide Portuguese inpatient database concerning the codification of pneumonia, with a special emphasis on identifying suspicious cases of upcoding affecting proper APR-DRG (All-Patient Refined Diagnosis-Related Groups) classification and hospital funding. Using data on pneumonia-related hospitalizations from 2015, we compared six hospitals with similar complexity regarding the frequency of all pneumonia-related diagnosis codes in order to identify codes that were significantly overreported in a given facility relatively to its peers. To verify whether the discrepant codes could be related to upcoding, we built Support Vector Machine (SVM) models to simulate the APR-DRG system and assess its response to each discrepant code. Findings demonstrate that hospitals significantly differed in coding six pneumonia conditions, with five of them playing a major role in increasing APR-DRG complexity, being thus suspicious cases of upcoding. However, those comprised a minority of cases and the overall credibility concerning upcoding of pneumonia was above 99% for all evaluated hospitals. Our findings can not only be relevant for planning future audit processes by signalizing errors impacting APR-DRG classification, but also for discussing credibility of administrative data, keeping in mind their impact on hospital financing. Hence, the main contribution of this paper is a reproducible method that can be employed to monitor the credibility and to promote data quality management in administrative databases

Why are Hospital Prices Different? An Examination of New York Hospital Reimbursement

In New York State, health care spending has steadily increased over the past 25 years, and is expected to continue increasing through 2020; this spending growth has translated directly to increases in health insurance premiums that can make health care unaffordable for consumers and adversely affect wages, employment, and economic growth. As policymakers work to ensure that the health care market functions in a way that maintains access to health care for New Yorkers and supports a competitive market for the industry, they may benefit from a better understanding of the various factors that influence these health care costs. To help inform policymakers and other stakeholders in New York, this study offers an in-depth examination of hospital contracting practices, reimbursement methodologies, and hospital prices in New York. Using information collected from private commercial health insurers and other sources, the study sheds light on how prices vary across hospitals and highlights certain practices that can inhibit healthy market competition. The report also suggests approaches to addressing some of these market dysfunctions. As the first study of its kind in New York, it introduces a range of opportunities for assisting policymakers and other stakeholders in understanding health care costs and developing strategies to slow cost growth

Addressing the Practice Context in Evidence-Based Practice Implementation: Leadership and Climate

Implementation of evidence-based practices (EBP) is complicated with barriers, many of which are associated with the context of care. However, little is known regarding social dynamic context factors (e.g., leadership and climate) that affect implementation. As leaders of patient care units, nurse managers have a pivotal role in fostering unit climates supportive of implementation of EBPs into care delivery; however, nurse managerial leadership and unit climate are widely overlooked in this area of science. The purposes of this study were to: 1) describe nurse manager EBP competencies, nurse manager EBP leadership behaviors, and unit climates for EBP implementation; 2) examine the unique contributions of nurse manager EBP leadership behaviors and nurse manager EBP competencies in explaining unit climate for EBP implementation; and 3) examine the unique contributions of these social dynamic context factors in explaining patient outcomes. A multi-site, multi-unit cross sectional design was used in this study. Institutional review board approvals at the investigator’s site and at each participating hospital were obtained prior to collecting data from a sample of 287 staff nurses and 23 nurse managers from 24 medical-surgical units in 7 acute care hospitals, geographically dispersed across the Northeast and Midwest United States. While controlling for key confounding variables and nested effects of units in hospitals, nurse manager EBP leadership behaviors (b= 0.64, p < .0001) and EBP competency (b=-0.22, p= .003) explained 50.2% of variance in unit climate for EBP implementation. In models explaining unit fall rates, unit climates for EBP implementation demonstrated the largest effect (b=-0.86, p<.01). Post hoc mediation analyses provided preliminary evidence suggesting the relationship between nurse manager EBP leadership behaviors and fall rates is mediated by unit climate for EBP implementation. The study identified a need for future work to address nurse manager EBP competency, nurse manager EBP leadership behaviors, and unit climates for EBP implementation in acute care medical-surgical units. This study is the first to describe nurse manager EBP competencies, nurse manager EBP leadership behaviors, and nursing unit climate for EBP implementation. Equipped with EBP leadership behaviors and competencies, nurse managers likely foster practice climates more conducive for EBP implementation resulting in patient receipt of evidence-based care and improved patient outcomes. Future work to develop interventions addressing these social dynamic context factors are needed as well as studies to test the effect of these context factors on implementation processes and outcomes.PHDNursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138458/1/clayshu_1.pd

Optimising cardiac services using routinely collected data and discrete event simulation

Background: The current practice of managing hospital resources, including beds, is very much driven by measuring past or expected utilisation of resources. This practice, however, doesn’t reflect variability among patients. Consequently, managers and clinicians cannot make fully informed decisions based upon these measures which are considered inadequate in planning and managing complex systems. Aim: to analyse how variation related to patient conditions and adverse events affect resource utilisation and operational performance. Methods: Data pertaining to cardiac patients (cardiothoracic and cardiology, n=2241) were collected from two major hospitals in Oman. Factors influential to resource utilisation were assessed using logistic regressions. Other analysis related to classifying patients based on their resource utilisation was carried out using decision tree to assist in predicting hospital stay. Finally, discrete event simulation modelling was used to evaluate how patient factors and postoperative complications are affecting operational performance. Results: 26.5% of the patients experienced prolonged Length of Stay (LOS) in intensive care units and 30% in the ward. Patients with prolonged postoperative LOS had 60% of the total patient days. Some of the factors that explained the largest amount of variance in resource use following cardiac procedure included body mass index, type of surgery, Cardiopulmonary Bypass (CPB) use, non-elective surgery, number of complications, blood transfusion, chronic heart failure, and previous angioplasty. Allocating resources based on patient expected LOS has resulted in a reduction of surgery cancellations and waiting times while overall throughput has increased. Complications had a significant effect on perioperative operational performance such as surgery cancellations. The effect was profound when complications occurred in the intensive care unit where a limited capacity was observed. Based on the simulation model, eliminating some complications can enlarge patient population. Conclusion: Integrating influential factors into resource planning through simulation modelling is an effective way to estimate and manage hospital capacity.Open Acces

External validation of decision-analytic models based on claims data of health insurance funds

Background: Decision-analytic models are used in the context of economic evaluation to bring together the best available evidence and to support the decision on the adoption of a health technology. A decision model’s credibility is, however, diminished by uncertainty which, to large part, stems from parameter uncertainty. Especially when novel technologies are evaluated, high quality evidence may not be available at the point of coverage decision making. A decision model incorporating uncertain parameter values eventually simulates uncertain effectiveness and cost outcomes. To enhance credibility of decision models, external validation of uncertain parameter values is vital. Data sources for external validation should be able to reflect the model’s study design and patient cohort, and estimate real-world effectiveness and costs. Objective: This study assesses whether claims data of health insurance funds are suitable to externally validate decision-analytic models. Methods: To answer the research question, a validation approach is developed which highlights critical steps in the validation of decision models based on claims data. The validation steps are: 1) selection of the validation level, 2) selection of the claims dataset, study design, and patient cohort, 3) selection of disease-relevant health technologies and costs, 4) statistical analysis of claims data, 5) changes to the decision model, 6) comparison between model and claims data, and 7) sensitivity analyses. The validation approach is exemplarily applied in the validation of a Markov model comparing treatment of localized prostate cancer (active surveillance and radical prostatectomy) in a German health care context, based on claims data of the German AOK statutory health insurance fund. An external validation of resource use, probability of utilization, and cost parameters is chosen, because these parameters are afflicted by a high degree of uncertainty in the decision model. Two different approaches to the analysis of relevant health technologies for prostate cancer treatment are presented in claims data analysis: an excess approach and a disease-related approach. Results: The decision model assumes that resource use and unit costs are identical in the two treatment groups; this is, however, not observed in claims data analysis. Excess cost analysis and disease-related cost analysis of AOK claims data as well as model analysis show that, overall, active surveillance is the less costly strategy compared to radical prostatectomy, with total incremental costs of €-6,611, €-6,260, and €-7,486 respectively. When testing differences between model and outcomes of claims data analysis, p-values of 0.61 (excess approach) and 0.18 (disease-related approach) indicate an agreement that is sufficient to assume that the decision model simulates real-world costs validly. Discussion: This study reveals general strengths and limitations of claims data based model validation. Claims data are able to provide evidence on real-world resource utilization and, with limitations regarding clinical information, effectiveness of a wide range of indications and treatments for a large patient cohort. Validation based on claims data is especially suitable when the decision maker, interested in the validity of the model in question, is the insurance fund providing access to the claims data. Suitability of claims data based validation is, however, limited concerning the replication of decision models’ structure and patient cohort. For one, the identification of distinct health states is limited, because clinical information is not included in sufficient detail. Secondly, due to non-randomization and a restricted number of variables available to adjust for confounding, comparability of treatment groups is limited in claims data analysis. Thirdly, distinct identification of health technology utilization and corresponding costs is not possible if the technology of interest is not specifically coded. Finally, claims data are, generally, collected for billing purposes; diagnoses and technology utilization are only coded if they are relevant for reimbursement by the insurance fund, which biases outcomes of model validation in cases where treatment is not covered by the insurance fund. Conclusion: The presented validation approach indicates critical aspects of the validation based on claims data, which may support researchers and decision makers in their decision on the suitability of claims data for model validation. The suitability of claims data for the external validation of a decision model ultimately depends on the ability of the claims data source to reflect the model’s patient cohort and outcome measures.Hintergrund: Entscheidungsanalytische Modelle kommen im Rahmen der gesundheitsökonomischen Evaluation von Gesundheitstechnologien zum Einsatz, um die beste verfügbare Evidenz zusammenzuführen und damit die Erstattungsentscheidung zu unterstützen. Bei der Evaluation von innovativen Technologien ist allerdings häufig zum Zeitpunkt der Erstattungsentscheidung keine hochwertige Evidenz, etwas aus klinischen Studien, verfügbar. Diese Parameterunsicherheit spiegelt sich letztlich in der im Entscheidungsmodell simulierten Kosteneffektivität der jeweiligen innovativen Technologien wieder. Für den Entscheidungsträger ist somit die Glaubwürdigkeit von Entscheidungsmodellen eingeschränkt. Um die Glaubwürdigkeit eines Entscheidungsmodells zu erhöhen, ist eine externe Validierung der unsicheren Parameterwerte von entscheidender Bedeutung. Datenquellen für eine externe Validierung sollten in der Lage sein, das Studiendesign und die Kohorte des Entscheidungsmodells zu reflektieren sowie reale Effekte und Kosten der evaluierten Technologie zu schätzen. Fragestellung: Im Rahmen dieser Studie wird untersucht, in wie weit sich Abrechnungsdaten von Krankenkassen für die externe Validierung von entscheidungsanalytischen Modellen eignen. Methoden: Um die Forschungsfrage zu beantworten, wurde ein Validierungsansatz entwickelt, welcher entscheidende Schritte bei der Validierung von Entscheidungsmodellen auf der Basis von Abrechnungsdaten beschreibt. Die einzelnen Validierungsschritte sind: 1) Auswahl der Validierungsebene, 2) Auswahl des externen Datensatzes, des Studiendesigns und der Patientenkohorte, 3) Definition von krankheitsrelevanten Gesundheitstechnologien und Kosten, 4) Auswahl der statistischen Methoden zur Analyse der Abrechnungsdaten, 5) Anpassung des Entscheidungsmodells, 6) Auswahl von Methoden zum Vergleich zwischen Modell und Abrechnungsdaten, und 7) Sensitivitätsanalysen. Der Validierungsansatz wird beispielhaft für die Validierung eines Markov-Modells angewendet, welches Behandlungsmethoden des lokalisierten Prostatakarzinoms (Active Surveillance und radikale Prostatektomie) in einem deutschen Versorgungskontext vergleicht. Zur Validierung werden Abrechnungsdaten einer deutschen gesetzlichen Krankenkasse, der AOK Baden-Württemberg, herangezogen. Es werden Parameterwerte des Entscheidungsmodells zum Ressourcenverbrauch, zur Inanspruchnahmewahrscheinlichkeit und zu Kosten validiert, da diese Parameter die größte Unsicherheit aufweisen. Dabei werden zwei verschiedene Vorgehensweisen zur Analyse der Abrechnungsdaten der AOK herangezogen: ein Excesskosten-Ansatz und ein Krankheitskosten-Ansatz. Ergebnisse: Im Entscheidungsmodell wird davon ausgegangen, dass Ressourcenverbrauch und Stückkosten in beiden Behandlungsgruppen identisch sind; in den Abrechnungsdaten der AOK ist diese Annahme allerdings nicht wiederzufinden. Sowohl die Excesskosten-Analyse und die krankheitskostenbezogene Analyse der AOK-Daten als auch die Modellanalyse zeigen, dass Active Surveillance insgesamt die kostengünstigere Strategie mit einer Ersparnis von jeweils 6.611€, 6.260€ und 7.486€ gegenüber der radikalen Prostatektomie ist. Der statistische Test der Kostendifferenz aus Modell und AOK-Daten ergibt p-Werte von 0,61 (Excesskosten-Ansatz) und 0,18 (Krankheitskosten-Ansatz), die auf eine signifikante Übereinstimmung der Schätzer aus Modell und AOK-Daten schließen lassen. Die Übereinstimmung der Schätzer lässt vermuten, dass das Entscheidungsmodell in der Lage ist, die Kosten der Behandlung des lokalisierten Prostatakarzinoms valide zu simulieren. Diskussion: Die beispielhafte Validierung des Markov-Modells anhand von Abrechnungsdaten der AOK Baden-Württemberg zeigt allgemeine Stärken und Schwächen der Kassendaten-basierten Modellvalidierung auf. Abrechnungsdaten sind in der Lage, Evidenz zur tatsächlichen Utilisierung von Gesundheitsleistungen und, mit Einschränkungen in Bezug auf klinische Informationen, Wirksamkeit einer Vielzahl von Behandlungsoptionen für eine große Patientenpopulation zu liefern. Die Validierung auf Basis von Abrechnungsdaten ist vor allem sinnvoll, wenn die Modellvalidierung aus der Perspektive einer Krankenkasse durchgeführt werden soll. Die Eignung von Abrechnungsdaten für die Modellvalidierung ist jedoch hinsichtlich der Nachbildung der Modellstruktur und der Patientenkohorte des Entscheidungsmodells limitiert. Erstens ist die Identifikation von Gesundheitszuständen in Kassendaten begrenzt, da klinische Informationen nicht ausreichend detailliert enthalten sind. Zweitens ist die Vergleichbarkeit der Behandlungsgruppen eingeschränkt, da eine Randomisierung nicht möglich ist und nur eine begrenzte Anzahl an Variablen zur Verfügung steht, um für Confounder zu adjustieren. Drittens ist eine eindeutige Identifizierung von Gesundheitsleistungen und deren Kosten schwierig, wenn die Leistung nicht explizit in den Abrechnungsdaten kodiert ist. Viertens werden Kassendaten zu Abrechnungszwecken gesammelt und deshalb werden auch nur solche Diagnosen und Gesundheitsleistungen kodiert, die für die Erstattung durch die Krankenkasse relevant sind. Für Gesundheitsleistungen, die nicht von der Krankenkasse vergütet werden, ist unter Umständen keine valide Schätzung zu Ressourcenverbrauch und Kosten möglich. Fazit: Der entwickelte Validierungsansatz zeigt kritische Aspekte der Modellvalidierung auf Basis von Abrechnungsdaten von Krankenkassen auf. Er soll Wissenschaftler und Entscheidungsträger bei der Entscheidung über die Eignung von Abrechnungsdaten für die externe Validierung eines Modells unterstützen. Die Eignung von Abrechnungsdaten für die externe Validierung eines Entscheidungsmodells hängt letztlich von der Fähigkeit ab, Modellstruktur, Kohorte und Zielparameter des Modells abzubilden

External validation of decision-analytic models based on claims data of health insurance funds

Background: Decision-analytic models are used in the context of economic evaluation to bring together the best available evidence and to support the decision on the adoption of a health technology. A decision model’s credibility is, however, diminished by uncertainty which, to large part, stems from parameter uncertainty. Especially when novel technologies are evaluated, high quality evidence may not be available at the point of coverage decision making. A decision model incorporating uncertain parameter values eventually simulates uncertain effectiveness and cost outcomes. To enhance credibility of decision models, external validation of uncertain parameter values is vital. Data sources for external validation should be able to reflect the model’s study design and patient cohort, and estimate real-world effectiveness and costs. Objective: This study assesses whether claims data of health insurance funds are suitable to externally validate decision-analytic models. Methods: To answer the research question, a validation approach is developed which highlights critical steps in the validation of decision models based on claims data. The validation steps are: 1) selection of the validation level, 2) selection of the claims dataset, study design, and patient cohort, 3) selection of disease-relevant health technologies and costs, 4) statistical analysis of claims data, 5) changes to the decision model, 6) comparison between model and claims data, and 7) sensitivity analyses. The validation approach is exemplarily applied in the validation of a Markov model comparing treatment of localized prostate cancer (active surveillance and radical prostatectomy) in a German health care context, based on claims data of the German AOK statutory health insurance fund. An external validation of resource use, probability of utilization, and cost parameters is chosen, because these parameters are afflicted by a high degree of uncertainty in the decision model. Two different approaches to the analysis of relevant health technologies for prostate cancer treatment are presented in claims data analysis: an excess approach and a disease-related approach. Results: The decision model assumes that resource use and unit costs are identical in the two treatment groups; this is, however, not observed in claims data analysis. Excess cost analysis and disease-related cost analysis of AOK claims data as well as model analysis show that, overall, active surveillance is the less costly strategy compared to radical prostatectomy, with total incremental costs of €-6,611, €-6,260, and €-7,486 respectively. When testing differences between model and outcomes of claims data analysis, p-values of 0.61 (excess approach) and 0.18 (disease-related approach) indicate an agreement that is sufficient to assume that the decision model simulates real-world costs validly. Discussion: This study reveals general strengths and limitations of claims data based model validation. Claims data are able to provide evidence on real-world resource utilization and, with limitations regarding clinical information, effectiveness of a wide range of indications and treatments for a large patient cohort. Validation based on claims data is especially suitable when the decision maker, interested in the validity of the model in question, is the insurance fund providing access to the claims data. Suitability of claims data based validation is, however, limited concerning the replication of decision models’ structure and patient cohort. For one, the identification of distinct health states is limited, because clinical information is not included in sufficient detail. Secondly, due to non-randomization and a restricted number of variables available to adjust for confounding, comparability of treatment groups is limited in claims data analysis. Thirdly, distinct identification of health technology utilization and corresponding costs is not possible if the technology of interest is not specifically coded. Finally, claims data are, generally, collected for billing purposes; diagnoses and technology utilization are only coded if they are relevant for reimbursement by the insurance fund, which biases outcomes of model validation in cases where treatment is not covered by the insurance fund. Conclusion: The presented validation approach indicates critical aspects of the validation based on claims data, which may support researchers and decision makers in their decision on the suitability of claims data for model validation. The suitability of claims data for the external validation of a decision model ultimately depends on the ability of the claims data source to reflect the model’s patient cohort and outcome measures.Hintergrund: Entscheidungsanalytische Modelle kommen im Rahmen der gesundheitsökonomischen Evaluation von Gesundheitstechnologien zum Einsatz, um die beste verfügbare Evidenz zusammenzuführen und damit die Erstattungsentscheidung zu unterstützen. Bei der Evaluation von innovativen Technologien ist allerdings häufig zum Zeitpunkt der Erstattungsentscheidung keine hochwertige Evidenz, etwas aus klinischen Studien, verfügbar. Diese Parameterunsicherheit spiegelt sich letztlich in der im Entscheidungsmodell simulierten Kosteneffektivität der jeweiligen innovativen Technologien wieder. Für den Entscheidungsträger ist somit die Glaubwürdigkeit von Entscheidungsmodellen eingeschränkt. Um die Glaubwürdigkeit eines Entscheidungsmodells zu erhöhen, ist eine externe Validierung der unsicheren Parameterwerte von entscheidender Bedeutung. Datenquellen für eine externe Validierung sollten in der Lage sein, das Studiendesign und die Kohorte des Entscheidungsmodells zu reflektieren sowie reale Effekte und Kosten der evaluierten Technologie zu schätzen. Fragestellung: Im Rahmen dieser Studie wird untersucht, in wie weit sich Abrechnungsdaten von Krankenkassen für die externe Validierung von entscheidungsanalytischen Modellen eignen. Methoden: Um die Forschungsfrage zu beantworten, wurde ein Validierungsansatz entwickelt, welcher entscheidende Schritte bei der Validierung von Entscheidungsmodellen auf der Basis von Abrechnungsdaten beschreibt. Die einzelnen Validierungsschritte sind: 1) Auswahl der Validierungsebene, 2) Auswahl des externen Datensatzes, des Studiendesigns und der Patientenkohorte, 3) Definition von krankheitsrelevanten Gesundheitstechnologien und Kosten, 4) Auswahl der statistischen Methoden zur Analyse der Abrechnungsdaten, 5) Anpassung des Entscheidungsmodells, 6) Auswahl von Methoden zum Vergleich zwischen Modell und Abrechnungsdaten, und 7) Sensitivitätsanalysen. Der Validierungsansatz wird beispielhaft für die Validierung eines Markov-Modells angewendet, welches Behandlungsmethoden des lokalisierten Prostatakarzinoms (Active Surveillance und radikale Prostatektomie) in einem deutschen Versorgungskontext vergleicht. Zur Validierung werden Abrechnungsdaten einer deutschen gesetzlichen Krankenkasse, der AOK Baden-Württemberg, herangezogen. Es werden Parameterwerte des Entscheidungsmodells zum Ressourcenverbrauch, zur Inanspruchnahmewahrscheinlichkeit und zu Kosten validiert, da diese Parameter die größte Unsicherheit aufweisen. Dabei werden zwei verschiedene Vorgehensweisen zur Analyse der Abrechnungsdaten der AOK herangezogen: ein Excesskosten-Ansatz und ein Krankheitskosten-Ansatz. Ergebnisse: Im Entscheidungsmodell wird davon ausgegangen, dass Ressourcenverbrauch und Stückkosten in beiden Behandlungsgruppen identisch sind; in den Abrechnungsdaten der AOK ist diese Annahme allerdings nicht wiederzufinden. Sowohl die Excesskosten-Analyse und die krankheitskostenbezogene Analyse der AOK-Daten als auch die Modellanalyse zeigen, dass Active Surveillance insgesamt die kostengünstigere Strategie mit einer Ersparnis von jeweils 6.611€, 6.260€ und 7.486€ gegenüber der radikalen Prostatektomie ist. Der statistische Test der Kostendifferenz aus Modell und AOK-Daten ergibt p-Werte von 0,61 (Excesskosten-Ansatz) und 0,18 (Krankheitskosten-Ansatz), die auf eine signifikante Übereinstimmung der Schätzer aus Modell und AOK-Daten schließen lassen. Die Übereinstimmung der Schätzer lässt vermuten, dass das Entscheidungsmodell in der Lage ist, die Kosten der Behandlung des lokalisierten Prostatakarzinoms valide zu simulieren. Diskussion: Die beispielhafte Validierung des Markov-Modells anhand von Abrechnungsdaten der AOK Baden-Württemberg zeigt allgemeine Stärken und Schwächen der Kassendaten-basierten Modellvalidierung auf. Abrechnungsdaten sind in der Lage, Evidenz zur tatsächlichen Utilisierung von Gesundheitsleistungen und, mit Einschränkungen in Bezug auf klinische Informationen, Wirksamkeit einer Vielzahl von Behandlungsoptionen für eine große Patientenpopulation zu liefern. Die Validierung auf Basis von Abrechnungsdaten ist vor allem sinnvoll, wenn die Modellvalidierung aus der Perspektive einer Krankenkasse durchgeführt werden soll. Die Eignung von Abrechnungsdaten für die Modellvalidierung ist jedoch hinsichtlich der Nachbildung der Modellstruktur und der Patientenkohorte des Entscheidungsmodells limitiert. Erstens ist die Identifikation von Gesundheitszuständen in Kassendaten begrenzt, da klinische Informationen nicht ausreichend detailliert enthalten sind. Zweitens ist die Vergleichbarkeit der Behandlungsgruppen eingeschränkt, da eine Randomisierung nicht möglich ist und nur eine begrenzte Anzahl an Variablen zur Verfügung steht, um für Confounder zu adjustieren. Drittens ist eine eindeutige Identifizierung von Gesundheitsleistungen und deren Kosten schwierig, wenn die Leistung nicht explizit in den Abrechnungsdaten kodiert ist. Viertens werden Kassendaten zu Abrechnungszwecken gesammelt und deshalb werden auch nur solche Diagnosen und Gesundheitsleistungen kodiert, die für die Erstattung durch die Krankenkasse relevant sind. Für Gesundheitsleistungen, die nicht von der Krankenkasse vergütet werden, ist unter Umständen keine valide Schätzung zu Ressourcenverbrauch und Kosten möglich. Fazit: Der entwickelte Validierungsansatz zeigt kritische Aspekte der Modellvalidierung auf Basis von Abrechnungsdaten von Krankenkassen auf. Er soll Wissenschaftler und Entscheidungsträger bei der Entscheidung über die Eignung von Abrechnungsdaten für die externe Validierung eines Modells unterstützen. Die Eignung von Abrechnungsdaten für die externe Validierung eines Entscheidungsmodells hängt letztlich von der Fähigkeit ab, Modellstruktur, Kohorte und Zielparameter des Modells abzubilden

THE IMPACT OF RURAL HOSPITAL-BASED MATERNITY UNIT CLOSURES IN NORTH CAROLINA

TBDDoctor of Philosoph

Healthcare associated infection surveillance: Examining influences on reliable and valid data collection and analysis

Healthcare settings are dangerous places. Individuals who receive healthcare may be subject to unintended harm as a consequence. One potential adverse event is a ‘healthcare associated infection’. This contemporary term refers to any infection which is acquired in healthcare facilities or any infection that occurs as a result of healthcare interventions. This thesis is concerned with the topic of healthcare associated infections. The effects of healthcare associated infections are felt not only by individual patients through increased morbidity and mortality but also by health services faced with higher costs associated with infections. The prevention of infection requires a multifaceted approach which is underpinned by healthcare-associated infection surveillance. Surveillance is used to influence practice and policy as well as to evaluate the effectiveness of strategies to reduce healthcare associated infections. Surveillance of healthcare associated infections is a critical element of any infection control program and it is crucial that healthcare-associated infection surveillance data are reliable and valid. In this thesis, three individual studies are presented. The three studies focus on two specific healthcare-associated infections: Staphylococcus aureus bacteraemia and Clostridium difficile infection. The aim of this thesis is to explore the epidemiology of these two infections and, in doing so, to examine methodological influences on reliable and valid healthcare associated infection data collection and analysis. The first study – an examination of the epidemiology of Staphylococcus aureus bacteraemia in Tasmania, Australia – used a descriptive, observational, population-based study design. This is the first known Australian study to capture and analyse data from all cases of SAB at a population-based level and represent this as an incidence. Four key findings can be identified from this study. First, the incidence of Staphylococcus aureus bacteraemia at a population level was accurately determined for the first time in Australia and was found to be 21.26 per 100,000 population, with 42% of Staphylococcus aureus bacteraemia being healthcare associated. Second, 55% of healthcare associated Staphylococcus aureus bacteraemia was associated with intravascular device management. Third, case definitions for healthcare associated Staphylococcus aureus bacteraemia have an influence on detection. Sixty-eight per cent of healthcare associated Staphylococcus aureus bacteraemia occurred in persons hospitalised less than 48 hours but had other criteria which resulted in them being defined as healthcare associated. Therefore, in cases where no criteria other than timeframe are used to define cases of SAB, approximately 30% of cases of SAB would be incorrectly identified as community associated. Fourth, 11% of Staphylococcus aureus bacteraemia were identified in private hospitals which fall outside the scope of almost all Staphylococcus aureus bacteraemia surveillance programs in Australia...

Dementia care in hospitals: costs and strategies

This report estimates the cost of dementia care in New South Wales public hospitals using a subset of data from the Hospital Dementia Services (HDS) Project conducted by the Australian Institute of Health and Welfare in conjunction with the University of New South Wales and the University of Canberra. New South Wales provides both system and population diversity to be broadly representative of the Australian hospital experience of people with dementia.The report also presents innovative strategies and practices being implemented in Australia and internationally, which might improve the quality and cost efficiency of dementia care in hospitals.Almost half (47%) of episodes for people with dementia did not have dementia recorded as a diagnosisThe study population includes 20,748 people with dementia who had a completed hospital stay including at least one night in a New South Wales public hospital in 2006-07. Identification and reporting of dementia is often poor in hospitals-for almost half of the episodes for people with dementia in this study, dementia was not recorded as either a principal or additional diagnosis.People with dementia generally stay in hospital longer and have higher associated costs of care Results from this study showed that people with dementia generally have a longer length of stay (LOS) within a hospital than other patients, leading to greater costs to the health system. Almost three-quarters of the reasons for hospital care included in this study involved a longer median LOS for people with dementia compared with people without dementia. The average cost of hospital care for people with dementia was higher than for people without dementia ($7,720 compared with$5,010 per episode, respectively). The total cost of care in New South Wales public hospitals for patients who had dementia in 2006-07 was estimated to be $462.9 million, of which around 35$162.5 million) may be associated with dementia.A range of strategies were identified that could improve outcomes for people with dementia and reduce the costs of care&nbsp;General strategies that might improve outcomes for people with dementia in hospitals were identified through a literature review, HDS site visits and the expert advisory group for this report. This review highlighted a number of different initiatives being implemented in a range of settings, including strategies outside the hospital, strategies within emergency departments, strategies within the hospital, cross-sectoral strategies and environmental strategies. All of these strategies are ultimately aimed at improving the care experience for people with dementia. The findings of the review of strategies suggest that a multifaceted and integrated approach between hospital, mental health, residential aged care and community services is most likely to ensure that dementia care is delivered in the most appropriate and beneficial setting for the patient