Health Information Technology Fundamentals

A recent study (link below) published by the Commonwealth Fund found that smaller primary care practices (small groups were divided into practice sizes of 1-2 physicians and 3-9 physicians) have significantly lower rates of preventable admissions than larger practices (10-19 physicians). This occurred despite the larger practice’s tendency to employ significant greater amounts of continuity of care measures including: the patient-centered medical home, guidelines based reminders, and electronic prescribing.   The study’s findings are limited by it being conducted via a survey of 1,045 primary care practices from July 2007 to March 2009, so its data preceded the wide-spread adoption of EHRs, HIEs, ACOs, and Meaningful Use.

Preventable Admission Rate Comparisons by Practice Size Compared to Practices of 10-19 physicians:

• 1-2 Physicians: 33% reduction
• 3-9 Physicians: 27% reduction

In general, consolidation of practices can lead to higher levels and access to specialty care and supports investments in health information technology, adherence to guidelines, and other benefits to patient care.  However, larger practices also have greater resources that allow them to invest in business practices. This study suggests that one of primary goals of consolidation, i.e., better coordination of care that facilitates keeping patients from being admitted unnecessary,  was not being met between 2007 and 2009.   Further study is needed to assess the impact of practice size and adoption of more modern quality of care policies and initiatives, including EHRs, e-Prescribing, ACOs, PCMH, and HIE.

Author: Michael Stearns, MD

Reference:  Commonwealth Fund Article: Small Primary Care Physician Practices Have Low Rates of Preventable Hospital Admissions

The New York Times article titled “For Big-Data Scientists, ‘Janitor Work’ is Key Hurdle to Insights” is a good read.  It provides an excellent overview of the challenges associated with making sense out of massive amounts of data being generated by disparate information systems.  The dramatic increase in the use of EHRs and related technologies in healthcare have fueled a data explosion that is unprecedented.  An avalanche of information has been coming from thousands of HIT systems using different languages.  Despite efforts going back over 30 years, the vast majority of data that is attached to any type of code is claims data (e.g., ICD-9-CM codes).   This type of structured data is notoriously unreliable in clinical care and only represents a small fraction of the data needed to make a difference in healthcare research and population management.

SNOMED CT, LOINC and other reference terminologies/ontologies attempt to give structure and meaning to information at the level of the concept.  The challenges faced by big data scientists would be markedly reduced if a greater amount of healthcare data was stored as codes using these types of terminologies.  Progress has been slow but Stage 2 Meaningful Use does offer hope that the adoption of SNOMED CT will increase, and that EHRs and other HIT systems will begin to adopt codified terminologies as the core language of their information systems.  These efforts have the potential to transform healthcare and genomic research.

The article can be found here: “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights”

Author of this post: Michael Stearns, MD