By Rosann M. O’Dell, D.H.Sc., MS, RHIA, CDIP
Twitter: @RosannMODell
In May of 1990, the 43rd World Health Assembly of the World Health Organization endorsed the International Classification of Diseases 10th Revision (ICD-10) and recommended it for international use. Since that time, the adoption of ICD-10 in the United States remains complicated despite United States participation at the World Health Assembly in 1990. As of the present day, a clinical modification of ICD-10 (or, the United States version ICD-10-CM/PCS) remains available but not yet implemented.
Copious commentary and writing exist related to the story of ICD-10 in the United States. Much of what has been said focuses on reimbursement, technical testing, and interoperability. The former are important and rightfully deserve attention. However, what is discussed less is the untapped value of morbidity data residing in the ICD-10 classification system.
The roots of clinical data classification are often overlooked and/or misunderstood. The London Bills of Mortality were among the earliest examples of an attempt to statistically classify clinical data; in the example of the London Bills of Mortality the ultimate goal was weekly reporting on causes of death. Later in the late 1800s, a statistician by the name of Jacques Bertillon sought a more succinct method to examine morbidity and mortality and created a classification known as the Bertillon Classification of Causes of Death. Bertillon’s classification was later adopted by the American Public Health Association and became the early basis for today’s International Classification of Diseases. In 1948 the World Health Organization assumed publication and leadership in the development of an internationally utilized classification with a goal that countries use a systematic approach in clinical data collection to appreciate the morbidity of their population and improve health service delivery, as well as maintain factual data on mortality. Overtime, the United States became one of the few countries linking coded clinical data to reimbursement.
The former annotated history lesson is relevant to today’s discussion about ICD-10. Most industries rely heavily, if not exclusively, on data to inform everything they do from the creation of service offerings and products to measuring quality and performance related factors that indicate success or the need for improvement. The United States maintains one of the financially costliest healthcare systems in the world. Another hallmark criticism of United States healthcare is that despite excessive spending on healthcare, patient outcomes remain difficult to measure.
A great deal of data is collected throughout healthcare in the United States measuring a variety of things. However, data remaining woefully untapped is that which precisely denotes the health status of individuals that can be aggregated and analyzed from patients seen at the individual provider level to informing the overarching public health infrastructure – and everything in between.
Until our healthcare system adopts ICD-10, we continue to classify clinical data with an outdated classification system (ICD-9) which inaccurately depicts many health conditions in relation to modern clinical practice and produces missed opportunities for granular detail in morbidity data. Without precise morbidity data, we continue to inaccurately understand what ails and complicates health status within our society, and how these factors are analyzed across the lifespan to inform and improve health and outcomes. The price of operating a healthcare system without good data is costly, impeding our ability for informed health policy and financing. The next time the online debate about ICD-10 focuses on the usual topics – testing, reimbursement, and readiness – please consider the issue of morbidity data; appreciate that our current approach to classifying clinical data hinders progress in measuring health status and outcomes, as well as contributes to health policy and financing that may actually be missing the mark. Precise morbidity data or lack thereof has far reaching implications that are worthy of attention.
About the Author: Rosann M. O’Dell, D.H.Sc., MS, RHIA, CDIP, is a Clinical Assistant Professor of Health Information Management at the University of Kansas Medical Center in Kansas City, Kansas, and an AHIMA Approved ICD-10-CM/PCS Trainer. Her interests include the intersection of clinical data, health outcomes, and policy. In addition to her work related to clinical data classification, she presents and publishes on a variety of topics such as consumer health informatics, ethics in healthcare, and emerging professional roles for health information practitioners. This article was originally published on HITECH Answers.