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International Classification of Diseases Hampers the Use of Analytics to Improve Health Care

By ANDY ORAM

andy oramThe health care field is in the grip of a standard that drains resources while infusing little back in return. Stuck in a paradigm that was defined in 1893 and never revised with regard for the promise offered by modern information processing, ICD symbolizes many of the fetters that keep the health industries from acting more intelligently and efficiently.

We are not going to escape the morass of ICD any time soon. As the “I” indicates in the title, the standard is an international one and the pace of change moves too slowly to be clocked.

In a period when hospitals are gasping to keep their heads above the surface of the water and need to invest in such improvements as analytics and standardized data exchange, the government has weighed them down with costs reaching hundreds of thousands of dollars, even millions just to upgrade from version 9 to 10 of ICD. An absurd appeal to Congress pushed the deadline back another year, penalizing the many institutions that had faithfully made the investment. But the problems of ICD will not be fixed by version 10, nor by version 11–they are fundamental to the committee’s disregard for the information needs of health institutions.

Disease is a multi-faceted and somewhat subjective topic. Among the aspects the health care providers must consider are these:

  • Disease may take years to pin down. At each visit, a person may be entering the doctor’s office with multiple competing diagnoses. Furthermore, each encounter may shift the balance of probability toward some diagnoses and away from others.
  • Disease evolves, sometimes in predictable ways. For instance, Parkinson’s and multiple sclerosis lead to various motor and speech problems that change over the decades.
  • Diseases are interrelated. For instance, obesity may be a factor in such different complaints as Type 2 diabetes and knee pain.

All these things have subtle impacts on treatment and–in the pay-for-value systems we are trying to institute in health care–should affect reimbursements. For instance, if we could run a program that tracked the shifting and coalescing interpretations that eventually lead to a patient’s definitive diagnosis, we might make the process take place much faster for future patients. But all a doctor can do currently is list conditions in a form such as:

E66.0 – Obesity due to excess calories

E11 – Type 2 diabetes mellitus

M25.562 – Pain in left knee

The tragedy is that today’s data analytics allow so much more sophistication in representing the ins and outs of disease.Take the issues of interrelations, for instance.

These are easy to visualize as graphs, a subject I covered recently.

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