Inpatient Utilization of Computed Tomography: the Influence of Market, Hospital, and Patient Characteristics

The use of computed tomography (CT) in the care of patients has grown dramatically since its introduction over 30 years ago. The vast majority of the utilization research has focused on factors associated with the variable use in the outpatient and emergency department settings. This has left much of the inpatient use and variation understudied. This study has multiple aims. The first is to characterize the inpatient variation across multiple states and markets. The second is to evaluate the relationship between inpatient CT use and commercial payers across these areas. The third is to develop a model to evaluate the relationship between inpatient CT use and the characteristics of markets, hospitals, and patients. The study uses a four-state convenience sample of cross-sectional data for hospitals. It included non-Federal, acute care hospitals that reported the performance of inpatient CT exams during 2015 (N=181). The literature review was used to justify the inclusion of variables in the study. The descriptive analyses were used to justify the appropriateness of the variables and methodology for testing. A comparison of means demonstrated the significant differences for inpatient utilization between states. A univariate general linear model demonstrated a negative relationship with a hospital’s proportion of commercially insured patients and the inpatient utilization rate. An ordinary least squares multivariate linear regression was used to test for variable significance within each of three constructs: markets, hospitals, and patients. The results indicated that inpatient CT rates were positively associated with higher level of insurer concentration (market), positively associated with system centralization (hospitals), and negatively associated with a hospital’s increasing proportion of minority patient discharges (patients). The study serves an important function in identifying varying patterns of CT utilization across the full spectrum of inpatients across multiple states, regardless of payer. It also creates new knowledge about how the characteristics of these markets, hospitals, and patients are related to inpatient use. It also provides implications for administrators, researchers, and policy makers. The additional knowledge and understanding provided by this research have the potential to lead to improvements in the appropriate and equitable use of inpatient CT exams

HOSX: Hospital operations excellence model

Hospital performance can be evaluated in four categories: (i) quality of care, (ii) process of care (iii) financial and (iv) operations productivity. Of these, ‘quality of care’ is the most widely reported and studied measure of performance, and focuses primarily on the clinical outcomes of the patient. In contrast, operations productivity and efficiency is the least studied measure, and currently there is limited ability to evaluate how efficiently the hospital has used its resources to deliver healthcare services. Cost containment in the healthcare industry is a challenging problem, and there is a lack of models and methods to benchmark hospital operating costs. Every hospital claims they are unique, and hence comparative assessments across hospitals cannot be made effectively. This research presents a performance framework for hospital operations to be called HOSx: Hospital Operations Excellence Model, used to measure and evaluate the operations productivity of hospitals. A key part of this research is healthcare activity data extracted from Medicare Provider Analysis and Review (MedPAR) database and the Healthcare Provider Cost Reporting Information System (HCRIS), both of which are maintained by the Center for Medicare Services (CMS). A key obstacle to hospital productivity measurement is defining a standard unit of output. Traditionally used units of output are inpatient day, adjusted patient day (APD) and adjusted discharge, which are reasonable estimators of patient volume, but are fundamentally limited in that they assume that all patients are equivalent. This research develops a standardized productivity output measure for a Hospital Unit of Care (HUC), which is defined as the resources required to provide one general medical/surgical inpatient day. The HUC model views patient care as a series of healthcare related activities that are designed to provide the needed quality of care for the specific disease. A healthcare activity is defined as a patient centric activity prescribed by physicians and requiring the direct use of hospital resources. These resources include (i) clinical staff (ii) non-clinical staff (iii) equipment (iv) supplies and (v) facilities plus other indirect resources. The approach followed here is to derive a roll-up equivalency parameter for each of the additional care/services activities that the hospital provides. Six HUC components are proposed: (i) case-mix adjusted inpatient days (ii) discharge disposition (iii) intensive care (iv) nursery (v) outpatient care and (vi) ancillary services. The HUC is compatible with the Medicare Cost Report data format. Model application is demonstrated on a set of 17 honor roll hospitals using data from MedPar 2011. An expanded application on 203 hospitals across multiple U.S. states shows that the HUC is significantly better correlated than the more traditional APD to hospital operating costs. The HUC measure will facilitate the development of an array of models and methods to benchmark hospital operating costs, productivity and efficiency. This research develops two hospital operations metrics. The first is the Hospital Resource Efficiency (HRE), which is defined as operating cost per Hospital Unit of Care, and the second is the Hospital Productivity Index, which benchmarks performance across the reference set of hospitals. Productivity analysis of all 203 hospitals in our database was conducted using these two measures. Specific factors studied include (i) functional areas (ii) patient volume (iii) geographical location. The results provide for the first time a ranking of most productive hospitals in each state – New Jersey, Pennsylvania, Nebraska, South Dakota and Washington as well as an interstate ranking. This research also provides detailed analysis of all outlier hospitals and causes of productivity variance in hospitals. The final output, the Hospital Total Performance Matrix combines clinical performance with productivity to identify the leading U.S. hospitals

Doctor of Philosophy

dissertationComputerized provider order entry (CPOE) is a component of electronic health records (EHR) that has been touted as a crucial means to support healthcare quality and efficiency. The costs of EHR implementation can be staggeringly high, and little literature exists to verify the hypothesized benefits of CPOE and EHRs. The purpose of this study, based on Coyle and Battle's adaptation of the classic Donabedian quality improvement framework, was to evaluate system-wide outcomes after CPOE implementation in a large academic setting. The specific aims were to describe the association between CPOE implementation and (1) mortality rate and (2) length of stay (LOS), controlling statistically for antecedent, structure, and process variables. The study used hierarchical linear modeling to analyze clinical and administrative data from 2.5 years before and 2.5 years after CPOE implementation. Aim 1 analysis included 104,153 hospital visits and aim 2 analysis included 89,818 visits. Two models were created for each analysis, (a) a model with individual patient care units as the unit of analysis and (b) a model with units aggregated by type. LOS decreased 0.9 days per visit in all models. Mortality decreased 1 to 4 deaths per 1000 visits, depending on the model; or 54 to 216 patient lives saved in the postimplementation period. Significant antecedents were patient demographics, insurance type, and scheduled versus emergency admission; structure variables included patient care unit, private room, and palliative care; and process variables included nursing care iv hours and the number of orders placed. Mortality models were variable by patient care unit, and strongly influenced by confounders such as rapid response team or code activation, suggesting the importance for future studies to account for those influences. CPOE was statistically associated with clinically significant improvements in the system-wide outcomes. Controlling statistically for antecedent, structure, and process variables, the analysis found that after the implementation of CPOE, there was a decrease in mortality and LOS. Future studies need to determine how CPOE implementation impacts nursing performance and how CPOE influences the effect of new physician resident arrival on patient outcomes

High performance in surgery

The national identification of high performing providers in surgery is of prime importance to patients, surgeons and commissioners of healthcare. This thesis explores how high performance is identified, defined and measured nationally and attempts to identify the factors that underlie high performance in colorectal cancer surgery during the peri-operative period. An introduction into the determinants of high performance in surgery as well as defining quality as it pertains to surgery is then undertaken. Identification of available national data sources and metrics for national performance are then identified. Comparison is made between voluntary and compulsory reporting systems highlighting greater capture of peri-operative mortality in compulsory reporting datasets. A novel marker that reflects outcome following complication management is developed. This marker is based on re-operations and is derived from compulsory reporting datasets. The use of non-operative re-interventions is then assessed in oesophago-gastric cancer resections as proof of concept. An appraisal of all colorectal cancer units in England is then undertaken using a panel of metrics demonstrating that analysis on a single marker alone may be too simplistic. Identifying factors that pertain to high performance beyond those available from routinely available datasets using a novel methodological approach called HiPer (High Performance) is performed. The interview based methodology identified rich qualitative factors in a group of colorectal cancer units worldwide that may be causal in their performance status. Finally, results from the interview study were related to hard outcome data from each unit which demonstrated some correlation between the HiPer methodology and the outcome data in the final section of the feasibility study. The implications of this may be that a dual approach of analysing routinely collected data with a more qualitative HiPer style methodology may help us better understand how high performing units achieve their results.Open Acces

Baystate Medical Practices Annual Report - 2018

https://scholarlycommons.libraryinfo.bhs.org/bmpannual_report/1003/thumbnail.jp

Changing payment instruments and the utilisation of new medical technologies

This paper empirically investigates the impact of additional reimbursement instruments on the diffusion of new technologies in inpatient care. Using 2010–2014 German panel data on hospital level for every patient undergoing coronary angioplasty, this study examines the utilisation of drug-eluting balloon catheters (DEB) over time while additional payment instruments changed. Hypothesising that the utilisation of DEB increased abruptly when a new reimbursement instrument came into force, we estimate a fixed effects regression comparing years with a change and years where the reimbursement instrument remained the same. The model is adjusted for patient age and severity of the disease. The utilisation of DEB increased from 8407 in 2010 to 19,065 in 2014. Hospitals used significantly more DEB when an additional payment instrument changed compared to years when it remained the same. The increase was roughly twice as large. In short, hospitals are incentivised to utilise new technologies if the reimbursement changes to an instrument that is designed in a more reliable way, e.g. including less bureaucracy or guaranteeing fixed prices.BMBF, 01EH1604A, Berliner Zentrum der Gesundheitsökonomischen Forschun

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Predictive modelling of hospital readmissions in diabetic patients clusters

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceDiabetes is a global public health problem with increasing incidence over the past 10 years. This disease's social and economic impacts are widely assessed worldwide, showing a direct and gradual decrease in the individual's ability to work, a gradual loss in the scale of quality of life and a burden on personal finances. The recurrence of hospitalisation is one of the most significant indexes in measuring the quality of care and the opportunity to optimise resources. Numerous techniques identify the patient who will need to be readmitted, such as LACE and HOSPITAL. The purpose of this study was to use a dataset related to the risk of hospital readmission in patients with Diabetes first to apply a clustering of subgroups by similarity. Then structures a predictive analysis with the main algorithms to identify the methodology of best performance. Numerous approaches were performed to prepare the dataset for these two interventions. The results found in the first phase were two clusters based on the total number of hospital recurrences and others on total administrative costs, with K=3. In the second phase, the best algorithm found was Neural Network 3, with a ROC of 0.68 and a misclassification rate of 0.37. When applied the same algorithm in the clusters, there were no gains in the confidence of the indexes, suggesting that there are no substantial gains in the division of subpopulations since the disease has the same behaviour and needs throughout its development

Measuring Provider Efficiency, Version 1.0, A Collaborative Multi-Stakeholder Effort

Provides accessible information about health provider efficiency, for use in creating a framework for measurement, and to act as a catalyst for stimulating the evolution of efficiency measurement as knowledge and understanding of the field grows

Investigating economic, design, and usability aspects of electronic health record systems

Following a 3-essay approach, this dissertation explores three aspects of healthcare IT using three different methodologies. In the first essay, we use econometric modeling to quantify the business value of information exchange using spillover mechanisms and discuss its implications in propagating sustained collaboration between ambulatory and tertiary care. Leveraging a nationwide sample of 3,483 US hospitals across 13 years, matched with approximately 30,000 ambulatory care facilities, we find that focal hospitals' inpatient cost per discharge decreases as EMR adoption by neighboring ambulatory facilities increases. Further, these effects are more substantial for urban, densely populated regions with more ambulatory entities that are proximal. This represents the bright side of EHR use. Next, the second essay uses a qualitative approach to understand the unintended consequences or dark side of EHR use. In this study, we interviewed 24 physicians across 11 specialties to understand what specific EHR characteristics cause stress among physicians. Following the standard qualitative coding process, we identify fifty-one design issues and ten stress-inducing EHR design themes that provide a deeper understanding of the technostress phenomenon. In addition, our findings can be used by EHR vendors to design better information systems. The final and third essay contributes to the lack of usability testing models and presents a proof of concept EHR usability evaluation model based on discrete event simulation techniques. Using literature-based workflow sequence and time-motion data assumptions, we show how to use simulation techniques to evaluate whether an EHR system delivers operational value in physician utilization. Usability evaluation is the first step in designing better EHR systems, and thus our proof-of-concept model can be used by EHR vendors and certification authorities to appraise the operational value of EHR applications. Overall, this dissertation investigates- 1) the bright side of EHR use that generates economic value for its users; 2) the dark side of EHR use that provides a deeper understanding of the physician burnout problem; 3) provides a solution that helps in designing better EHR systems while mitigating its unintended consequences