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Author: Daniel W. Gall
Publisher:
ISBN:
Category : Diagnosis related groups
Languages : en
Pages : 0
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Book Description
The Military Health system (MHS) has implemented a relatively new automated information system to help capture diagnoses, procedures, and insurance data for all ambulatory encounters. This system, recently implemented at Walter Reed Army Medical Center (WRAMC), is called the Ambulatory Data System (ADS). While the current MHS metric for ADS focuses on compliance, the quality of the data has yet to be extensively measured. Hence, the purpose of this project was to statistically analyze the data quality by studying the coding accuracy of International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes, Evaluation and Management (E & M) codes, and insurance indicator codes within the General Internal Medicine Clinic (GIMC) at WRAMC. Interventions to improve the data quality were identified, developed, and implemented during the course of this project. The sensitivities, positive predictive values (PPVs), and the kappa statistics determined from random samples collected before and after the interventions were compared using Chi square analysis (alpha=05). The results showed an increase in overall non-adjusted ICD-9-CM coding rates from 60% to 67% sensitivity, 66% to 73% PPV, and kappa=.18-.36, however, the differences were not significant. E & M coding improved from a poor sensitivity rate of 21% to significantly better rate of 55%. The study also identified a poor level of accuracy concerning the capture of insurance information (kappas=.18 and .16) that conservatively indicated approximately $1.35 million dollars of missed third party collections within the past year. This study provides a model to improve the quality ADS data that may enhance the organization's ability to efficiently and effectively identify clinical process improvements, make sound resource allocation decisions, increase third party collections, and conduct outcomes research.
Author: Daniel W. Gall
Publisher:
ISBN:
Category : Diagnosis related groups
Languages : en
Pages : 0
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Book Description
The Military Health system (MHS) has implemented a relatively new automated information system to help capture diagnoses, procedures, and insurance data for all ambulatory encounters. This system, recently implemented at Walter Reed Army Medical Center (WRAMC), is called the Ambulatory Data System (ADS). While the current MHS metric for ADS focuses on compliance, the quality of the data has yet to be extensively measured. Hence, the purpose of this project was to statistically analyze the data quality by studying the coding accuracy of International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes, Evaluation and Management (E & M) codes, and insurance indicator codes within the General Internal Medicine Clinic (GIMC) at WRAMC. Interventions to improve the data quality were identified, developed, and implemented during the course of this project. The sensitivities, positive predictive values (PPVs), and the kappa statistics determined from random samples collected before and after the interventions were compared using Chi square analysis (alpha=05). The results showed an increase in overall non-adjusted ICD-9-CM coding rates from 60% to 67% sensitivity, 66% to 73% PPV, and kappa=.18-.36, however, the differences were not significant. E & M coding improved from a poor sensitivity rate of 21% to significantly better rate of 55%. The study also identified a poor level of accuracy concerning the capture of insurance information (kappas=.18 and .16) that conservatively indicated approximately $1.35 million dollars of missed third party collections within the past year. This study provides a model to improve the quality ADS data that may enhance the organization's ability to efficiently and effectively identify clinical process improvements, make sound resource allocation decisions, increase third party collections, and conduct outcomes research.
Author: Daniel W. Gall
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author:
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ISBN:
Category :
Languages : en
Pages : 0
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The purpose of this project was to determine the coding accuracy of the Evaluation and Management (E & M), diagnosis, and procedure codes as reported in the Ambulatory Data System (ADS) for the Internal Medicine Clinic at Bayne-Jones Army Community Hospital, Fort Polk, Louisiana. A secondary purpose was to determine the financial impact of inaccurate coding through the use of the Ambulatory Payment Classification (APC) and Ambulatory Patient Group (APG) outpatient prospective payment system (OPPS). Three data sets were examined at different points in time based on type of ADS used and implementation of data quality management efforts. The data quality management efforts included training, template development, customization of diagnosis and procedure code selection lists, and management controls. The management controls consisted of the ADS compliance metric, KG-ADS reports, and ad hoc ADS reports. Analysis of the data revealed that the use of data quality management efforts did increase the accuracy of data in the ADS database. Analysis of the financial data indicated that the hospital was minimally at risk for either fraudulent billing or loss of revenue. However, as the Internal Medicine Clinic only accounts for 6.3% of outpatient workload, coding behaviors similar to those observed practiced in other high volume clinics would result in an increased annual impact. In order for data quality to be important for the staff of the hospital, command support of data quality management efforts is imperative. Command support combined with aggressive training on coding procedures and medical record documentation and the use of management controls will significantly increase the quality of data, thus decreasing the probability of inappropriate resource levels for mission accomplishment.
Author: Agency for Healthcare Research and Quality/AHRQ
Publisher: Government Printing Office
ISBN: 1587634333
Category : Medical
Languages : en
Pages : 385
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Book Description
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
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Book Description
Information derived from accurate, current, and complete patient encounter data is an invaluable resource for health care organizations today. Used to support many critical decision-making processes, organizations must strive to produce the highest-quality encounter data possible. This study examines the value of employing certified professional coders to collect encounter data. A comparison of two coding scenarios was conducted: coding completed by health care providers and coding completed by professional coders. Samples of Evaluation and Management codes applied by providers and coders were categorized into frequency distributions and the average standard deviations of these distributions were compared. As much less variation was found among the group of professional coders, it was concluded that they produced more reliable results and, therefore, improved coding data quality. In addition to this comparison, two previous studies that assessed the value of coders in ambulatory health care systems are included. These studies indicate that professional coders produce significant improvements in coding data quality.
Author: Karen Smiley
Publisher: John Wiley & Sons
ISBN: 1118982541
Category : Medical
Languages : en
Pages : 368
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Book Description
Your complete guide to a career in medical billing and coding, updated with the latest changes in the ICD-10 and PPS This fully updated second edition of Medical Billing & Coding For Dummies provides readers with a complete overview of what to expect and how to succeed in a career in medical billing and coding. With healthcare providers moving more rapidly to electronic record systems, data accuracy and efficient data processing is more important than ever. Medical Billing & Coding For Dummies gives you everything you need to know to get started in medical billing and coding. This updated resource includes details on the most current industry changes in ICD-10 (10th revision of the International Statistical Classification of Diseases and Related Health Problems) and PPS (Prospective Payment Systems), expanded coverage on the differences between EHRs and MHRs, the latest certification requirements and standard industry practices, and updated tips and advice for dealing with government agencies and insurance companies. Prepare for a successful career in medical billing and coding Get the latest updates on changes in the ICD-10 and PPS Understand how the industry is changing and learn how to stay ahead of the curve Learn about flexible employment options in this rapidly growing industry Medical Billing & Coding For Dummies, 2nd Edition provides aspiring professionals with detailed information and advice on what to expect in a billing and coding career, ways to find a training program, certification options, and ways to stay competitive in the field.
Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309140129
Category : Medical
Languages : en
Pages : 286
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Book Description
The goal of eliminating disparities in health care in the United States remains elusive. Even as quality improves on specific measures, disparities often persist. Addressing these disparities must begin with the fundamental step of bringing the nature of the disparities and the groups at risk for those disparities to light by collecting health care quality information stratified by race, ethnicity and language data. Then attention can be focused on where interventions might be best applied, and on planning and evaluating those efforts to inform the development of policy and the application of resources. A lack of standardization of categories for race, ethnicity, and language data has been suggested as one obstacle to achieving more widespread collection and utilization of these data. Race, Ethnicity, and Language Data identifies current models for collecting and coding race, ethnicity, and language data; reviews challenges involved in obtaining these data, and makes recommendations for a nationally standardized approach for use in health care quality improvement.
Author:
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Category :
Languages : en
Pages : 0
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In a major step towards meeting Health Insurance Potability and Accountability Act (HIPAA) standards, the Military Healthcare System (MHS)plans to implement an outpatient itemized billing system by October 2002. Over the last four years, there have been many different methods and systems used to collect and code outpatient encounter data. The purpose of this project is to evaluate and assess various methods of performing coding in the Walter Reed Army Medical Center (WRAMC) General Internal Medicine Clinic (GIMC)and determine whether current outpatient coding practice and data quality is sufficient for supporting itemized billing. The first part of this study involved a comparison on coding accuracy between providers in 1998 using a bubble sheet to code diagnoses and procedures (Gall, 1998), and the current study using an automated coding system. The results showed a decrease in International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis coding correctness from 66% to 51% and a decrease in the average number of diagnoses recorded per encounter from 2.24 to 1.81. Evaluation and management (E & M) complexity coding accuracy worsened, showing a higher propensity towards over coding, increasing from 37% to 83% over coded. The second part of this study evaluated coding accuracy of a clinic initiative using medical clerks to code directly from provider written documentation. Evaluating and comparing the results using these and other methods of coding is essential to developing the best practices for accurate coding in the MHS. This study provides suggested interventions and process improvements to assist the organization in improving coding accuracy and overall data quality. More importantly, these interventions will help leadership reduce billing risk and remain focused on the core mission of providing quality care to military beneficiaries.
Author: Lou Ann Schraffenberger
Publisher:
ISBN:
Category : Medical records
Languages : en
Pages : 376
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Book Description
This is a resource for coding managers across the continuum of health care settings, with a special emphasis on acute-care and hospital-based ambulatory settings. Addresses the scope and organization of clinical coding data, including standards for electronic record systems, coding practice issues, recruitment and retention of coding staff, chargemaster, management, quality control issues, compliance reporting issues, and financial issues. Contains numerous examples and case studies to illustrate issues and demonstrate how coding decisions affect other areas in the health care organization.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309377722
Category : Medical
Languages : en
Pages : 473
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Book Description
Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.
