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The Power of Claims Data - Big Data and Analytics Meet Health IT


Big Data, Analytics and Health IT create some amazing project opportunities for organizations that work with large volumes of diagnosis and treatment data. New data storage, processing and analysis tools have helped propel Health IT innovation to new levels. Health Insurance Carriers, Hospital Systems, Claims Processors and the Government are data rich with an abundance of diagnosis and treatment data. Big Data and Analytics can provide big project payoffs in many of these data rich areas.


Highlighting the potential size of opportunity from this data rich source, the Centers for Medicare & Medicaid Services reported the National Health Expenditure in 2014 grew 5.3% to $3.0 trillion, Medicare spending grew 5.5% to $618.7 billion, Private Health Insurance spending grew 4.4% to $991.0 billion in 2014, Out of Pocket spending grew 1.3% to $329.8 billion, Hospital expenditures grew 4.1% to $971.8 billion, Physician and Clinical Services expenditures grew 4.6% to $603.7 billion and Prescription Drug spending increased 12.2% to $297.7 billion (1).

With such large dollars involved representing individual medical and health services, a small positive impact in any of these numbers translates into huge benefits to consumers, taxpayers, providers, government and the insurance industry.

Diagnosis and Treatment Data Primer

Diagnosis and Treatment data comes in a few standard code sets within the United States including ICD-10-CM, CPT, ICD-10-PCS, DRG and HCPCS. If you have ever reviewed a medical bill you may have noticed some of these codes. In the United States many medical or health providers are compensated by insurance companies for services rendered (CPT Codes) to treat medical conditions (ICD Codes).

ICD-10-CM

International Statistical Classification of Diseases and Related Health Problems or ICD Codes are a group of numbers and letters that classify diseases and medical conditions. In the United States ICD-10-CM codes are utilized as the standard for all medical or health practitioners. Per the Centers for Disease Control (CDC) there are 69,823 codes.

The Structure of an ICD-10 code is up to seven (7) digits.


Digit 1 is alpha

Digit 2 is numeric

Digits 3 is alpha or numeric

A decimal is used after the third character

Digits 4–7 are alpha or numeric (alpha characters are not case sensitive)

Example codes:

  • S52.131A – Displaced fracture of neck of right radius, initial encounter for closed fracture

  • T15.02xD – Foreign body in cornea, left eye, subsequent encounter

  • E11.21 - Type 2 diabetes mellitus with diabetic nephropathy

The ICD-10 is copyrighted by the World Health Organization (WHO), which owns and publishes the code set and has allowed The National Center for Health Statistics (NCHS) development of an adaptation for use in the United States. To gain access to the list of ICD-10-CM codes or use in a project visit the CDC Website for the most up to date codes and usage information (2).

CPT Codes

Current Procedural Terminology or CPT Codes are a group of numbers, usually five (5) digits that classify medical and diagnostic procedures and services performed by medical or health practitioners.

CPT Codes are divided into three main categories

Category I - Procedures that are consistent with contemporary medical practice and are widely performed.

  • Codes for evaluation and management: 99201–99499

  • Codes for anesthesia: 00100–01999; 99100–99150

  • Codes for surgery: 10000–69990

  • Codes for Radiology: 70000-79999

  • Codes for pathology and laboratory: 80000–89398

  • Codes for medicine: 90281–99099; 99151–99199; 99500–99607

Category II - Supplemental tracking codes that are intended to be used for performance measurement. The last digit is an “F”.

Category III - The codes are intended to be temporary and will be retired if the procedure or service is not accepted as a Category I code within five years. The last digit is a “T”.

Example codes:

  • 43259 - Esophagogastroduodenoscopy, flexible, transoral; EUS of esophagus, stomach AND duodenum

  • 30420 - Rhinoplasty, primary; including major septal repair

  • 0007F - Beta-blocker therapy, prescribed

  • 0012T - Arthroscopy knee, surgical, implantation of osteochondral graft(s) for treatment of articular surface defect, autografts

The CPT code set is a registered trademark, owned and published by the American Medical Association. To gain access to the list of CPT codes or use in a project visit the AMA Website for the most up to date codes and usage information (3).

Real Results

Medical care facilities (hospitals, urgent care clinics, physicians’ offices, treatment centers, etc.) generate an immense amount of diagnosis and treatment data, information that Big Data coupled with the Analytics can provide insight into:

  • Fraud Detection

  • Disease and Outbreak Surveillance

  • Targeted Education

  • Medical Resource Allocation

  • Service and Reimbursement Costs

  • Underwriting Performance

The opportunity for fraud detection solutions alone is significant, a 2014 article published in the Economist, Donald Berwick, a former head of the Centers for Medicare and Medicaid Services (CMS), and Andrew Hackbarth of the RAND Corporation, estimated that fraud adds up to $272 billion across the entire health system (4).

To help facilitate the many benefits of leveraging diagnosis and treatment data many states have established or are in the process of implementing all-payer claims databases (APCDs) (5). To create an APCD, the state requires all commercial insurance carriers doing business within their borders to provide all claims data. This data then can be utilized in many ways including to help consumers identify dissimilar prices medical professionals charge for identical procedures, assist insurance and self-insured companies design cost-effective benefit plans and permit government shape responsive and representative public policy.

The State of Alaska, Health Care Commission in 2013 published a comprehensive All Payer Claims Database Study (6) which highlighted many successful projects that leveraged APCD data. One such example is the success of New Hampshire’s HealthCost website, which provides residents a web based tool to compare estimated health care costs for medical and dental services in the state by insurance plan and by procedure (7).


NH HealthCost Website, http://nhhealthcost.nh.gov/

This exciting area of convergence between Big Data, Analytics and Health IT has demonstrated the ability to produce results today and the promise for even greater impact in the future as more data becomes available and advancements in technology further progress.

References and Citations:

  1. Centers for Medicare & Medicaid Services Website, https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html, 3/24/2016

  2. Centers For Disease Control (CDC) Website, http://www.cdc.gov/nchs/icd/icd10cm.htm, 3/24/2016

  3. American Medical Association Website, About CPT, http://www.ama-assn.org/ama/pub/physician-resources/solutions-managing-your-practice/coding-billing-insurance/cpt/about-cpt.page?, 3/24/2016

  4. The Economist, “Health-care fraud, The $272 billion swindle”, 5/31/2014, http://www.economist.com/news/united-states/21603078-why-thieves-love-americas-health-care-system-272-billion-swindle

  5. All Payers Claims Data Council Website, https://www.apcdcouncil.org/state/map, 3/24/2016

  6. The State of Alaska, Health Care Commission, “All Payer Claims Database Study”, http://dhss.alaska.gov/ahcc/Documents/meetings/201303/AK-APCD-FeasibilityReport20131402.pdf, 3/24/2016

  7. New Hampshire Insurance Department, NH HealthCost Website, http://nhhealthcost.nh.gov/, 3/24/2016

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