Examining the Transitional Impact of ICD-10 on Healthcare Fraud Detection

On October 1st, 2015, the tenth revision of the International Classification of Diseases (ICD-10) will be mandatorily implemented in the United States. Although this medical classification system will allow healthcare professionals to code with greater accuracy, specificity, and detail, these codes will have a significant impact on the flavor of healthcare insurance claims. While the overall benefit of ICD-10 throughout the healthcare industry is unquestionable, some experts believe healthcare fraud detection and prevention could experience an initial drop in performance due to the implementation of ICD-10. We aim to quantitatively test the validity of this concern regarding an adverse transitional impact. This project explores how predictive fraud detection systems developed using ICD-9 claims data will initially react to the introduction of ICD-10. We have developed a basic fraud detection system incorporating both unsupervised and supervised learning methods in order to examine the potential fraudulence of both ICD-9 and ICD-10 claims in a predictive environment. Using this system, we are able to analyze the ability and performance of statistical methods trained using ICD-9 data to properly identify fraudulent ICD-10 claims. This research makes contributions to the domains of medical coding, healthcare informatics, and fraud detection

Health ManagementInformation Systems for Resource Allocation and Purchasing in Developing Countries

World Bank, Health Nutrition and Population, Discussion Paper: The paper begins with the premise that it is not possible to implement an efficient, modern RAP strategy today without the effective use of information technology. The paper then leads the architect through the functionality of the systems components and environment needed to support RAP, pausing to justify them at each step. The paper can be used as a long-term guide through the systems development process as it is not necessary (and likely not possible) to implement all functions at once. The paper’s intended audience is those members of a planning and strategy body, working in conjunction with technical experts, who are charged with designing and implementing a RAP strategy in a developing country

Tracking Foodborne Pathogens from Farm to Table: Data Needs to Evaluate Control Options

Food safety policymakers and scientists came together at a conference in January 1995 to evaluate data available for analyzing control of foodborne microbial pathogens. This proceedings starts with data regarding human illnesses associated with foodborne pathogens and moves backwards in the food chain to examine pathogen data in the processing sector and at the farm level. Of special concern is the inability to link pathogen data throughout the food chain. Analytical tools to evaluate the impact of changing production and consumption practices on foodborne disease risks and their economic consequences are presented. The available data are examined to see how well they meet current analytical needs to support policy analysis. The policymaker roundtable highlights the tradeoffs involved in funding databases, the economic evaluation of USDA's Hazard Analysis Critical Control Point (HACCP) proposal and other food safety policy issues, and the necessity of a multidisciplinary approach toward improving food safety databases.food safety, cost benefit analysis, foodborne disease risk, foodborne pathogens, Hazard Analysis Critical Control Point (HACCP), probabilistic scenario analysis, fault-tree analysis, Food Consumption/Nutrition/Food Safety,

Differences in Outcomes for Incarcerated and Non-Incarcerated Patients Hospitalized in the Commonwealth of Massachusetts, 2011-2013: Is “Adequate Care” in Criminal Justice Institutions Enough?

Objective: This study used data from the Healthcare Cost and Utilization Project State Inpatient Databases to identify whether inmates in Massachusetts had any differences in morbidity, mortality, cost, length of stay, and ambulatory care sensitive conditions as compared to a propensity-score matched (1:1 ratio) group of non-inmate patients. Methods: Differences were examined using t tests for continuous variables and Chisquare (χ2) tests for categorical variables. Multiple linear and logistic regression models were used to investigate relationships between the outcome variables and inmate/noninmate status, controlling for age, Charlson Comorbidity Index score, gender, primary payer, race, psychological conditions, suicide, and injuries. Results: On average inmates stayed 2.48 days longer in the hospital (10.40 vs. 7.92; p = \u3c.0001), their bill was $1,691 more ($10,226 vs. $8,535; p = \u3c.0001), and they had more chronic conditions (4.46 vs. 4.31; p =.0019) compared to non-inmate counterparts. Conclusion: The provision of healthcare to inmates is required by law, paid for by taxpayers, and managed differently at each correctional institution. Findings indicate care may not be adequate, requiring collaborative efforts to improve the provision and management of healthcare at correctional institutions

CPA\u27s guide to medical, dental and other healthcare practices;

CD-ROM files converted to PDF and included after main texthttps://egrove.olemiss.edu/aicpa_guides/1128/thumbnail.jp

Data Analytics of Codified Patient Data: Identifying Factors Influencing Coding Trends, Productivity, and Quality

Cost containment and quality of care have always been major challenges to the health care delivery system in the United States. Health care organizations utilize coded clinical data for health care monitoring, and reporting that includes a wide range of diseases and clinical conditions along with adverse events that could occur to patients during hospitalization. Furthermore, coded clinical data is utilized for patient safety and quality of care assessment in addition to research, education, resource allocation, and health service planning. Thus, it is critical to maintain high quality standards of clinical data and promote funding of health care research that addresses clinical data quality due to its direct impact on individual health outcomes as well as population health. This dissertation research is aimed at identifying current coding trends and other factors that could influence coding quality and productivity through two major emphases: (1) quality of coded clinical data; and (2) productivity of clinical coding. It has adopted a mix-method approach utilizing varied quantitative and qualitative data analysis techniques. Data analysis includes a wide range of univariate, bivariate, and multivariate analyses. Results of this study have shown that length of stay (LOS), case mix index (CMI) and DRG relative weight were not found to be significant predictors of coding quality. Based on the qualitative analysis, history and physical (H&P), discharge summary, and progress notes were identified as the three most common resources cited by Ciox auditors for coding changes. Also, results have shown that coding productivity in ICD-10 is improving over time. Length of stay, case mix index, DRG weight, and bed size were found to have a significant impact on coding productivity. Data related to coder’s demographics could not be secured for this analysis. However, factors related to coders such as education, credentials, and years of experience are believed to have a significant impact on coding quality as well as productivity. Linking coder’s demographics to coding quality and productivity data represents a promising area for future research

Longitudinal Patient Records: A Re-Examination of the Possibility

It has long been recognized that the Longitudinal Patient Record (LPR) has been defined as “A life-long incremental process where each clinical encounter is merely an updating of the file” (Gabrieli, 1997) Understanding the health condition of patient longitudinally is very important to the care of the patient. However, it is not clear to what extent a longitudinal patient record is in fact possible, since a true longitudinal patient record would need to include all information for a patient, from cradle to grave, across all healthcare providers and systems, across all corporate or geographic or national boundaries. Compiling or maintaining such a record is a problem of staggering practical difficulties. Yet, there is no doubt of the potential benefit to the patient of the availability of such a record to the patient’s caregivers and providers. In this thesis, we re-examine the possibility of a longitudinal patient record, both in its pure logical sense, and in a practical sense. One point of view that we stress is to model the longitudinal patient record not so much as a static thing, but rather as a functional entity. That is, the longitudinal patient record is understood as a set of processes that provide the physician or other clinician decision maker (or for that matter the patient himself) with whatever longitudinal view of the patient information is available and practical to serve the current context of decision making. That is, the model we suggest is one of making the most out of whatever patient information is available to the decision maker

DataGauge: A Model-Driven Framework for Systematically Assessing the Quality of Clinical Data for Secondary Use

There is growing interest in the reuse of clinical data for research and clinical healthcare quality improvement. However, direct analysis of clinical data sets can yield misleading results. Data Cleaning is often employed as a means to detect and fix data issues during analysis but this approach lacks of systematicity. Data Quality (DQ) assessments are a more thorough way of spotting threats to the validity of analytical results stemming from data repurposing. This is because DQ assessments aim to evaluate ‘fitness for purpose’. However, there is currently no systematic method to assess DQ for the secondary analysis of clinical data. In this dissertation I present DataGauge, a framework to address this gap in the state of the art. I begin by introducing the problem and its general significance to the field of biomedical and clinical informatics (Chapter 1). I then present a literature review that surveys current methods for the DQ assessment of repurposed clinical data and derive the features required to advance the state of the art (Chapter 2). In chapter 3 I present DataGauge, a model-driven framework for systematically assessing the quality of repurposed clinical data, which addresses current limitations in the state of the art. Chapter 4 describes the development of a guidance framework to ensure the systematicity of DQ assessment design. I then evaluate DataGauge’s ability to flag potential DQ issues in comparison to a systematic state of the art method. DataGauge was able to increase ten fold the number of potential DQ issues found over the systematic state of the art method. It identified more specific issues that were a direct threat to fitness for purpose, but also provided broader coverage of the clinical data types and knowledge domains involved in secondary analyses. DataGauge sets the groundwork for systematic and purpose-specific DQ assessments that fully integrate with secondary analysis workflows. It also promotes a team-based approach and the explicit definition of DQ requirements to support communication and transparent reporting of DQ results. Overall, this work provides tools that pave the way to a deeper understanding of repurposed clinical dataset limitations before analysis. It is also a first step towards the automation of purpose-specific DQ assessments for the secondary use of clinical data. Future work will consist of further development of these methods and validating them with research teams making secondary use of clinical data