Additions to the paper!

Mark Weiner

7/28/08

Bridging data across systems made easier by centralization

Many institutions have electronic clinical data that is decentralized across different departments. That infrastructure can be used to create an integrated data set with information that spans the source systems.  However, the decentralization creates the need for redundant steps, lengthy iterative processes to refine the information, and requires that more people have access to protected health information in order to satisfy the research information need.  To illustrate these issues, the following describes the  workflow needed to define a cohort of people known to have cardiovascular disease and laboratory evidence of significant renal disease defined by an elevated serum creatinine. 

In the decentralized system, where should the investigator start?  He can begin by going to the billing system that stores diagnoses and get a list of PHI of people  with a history of a heart attack.  Then he can transport that list of identifiers the people who work in the laboratory and request the serum creatinine levels on that set of patients, and then limit the list to those who have an elevation.  The lab will have to validate the patient matches generated by the billing system by comparing PHI, a step redundant with the billing system.  Furthermore, many of the subjects associated with heart attack may not have the elevated creatinine, so, in retrospect, the PHI of these people should not have been available to the people running the query in the lab.   Perhaps the cohort that was generated was not as large as expected, and the investigator decides to expand the cohort to those patients with a diagnosis of peripheral vascular disease and stroke.  He then has to iterate back to the billing system to draw additional people with these additional diagnoses, then bring the new list of patient identifiers to the lab to explore their creatinine levels. 

The centralized warehouse as proposed will conduct the matching of patient identifiers behind the scenes.  The information system will conduct the matching of patients across the different components of the database, so that identifiers do not have to be manually transported and manipulated by the distinct database managers at each location.  Further, if the query produces results that are not satisfactory, the cycle of re-querying the data with new criteria will be faster, and user controlled.

Shrinking of the Data Collection Phase

The unique information needs of research will likely always require the need for custom data collection in the form of interviews and surveys.  However, electronic health records and personal health records and changing the concept of routinely collected data available for research.  Patient information that was previously only present on messy handwritten patient diaries can now be input into discrete fields in a personal health record.  Vital signs and medication changes that may or may not have been well recorded in a paper chart are now routinely recorded in electronic records.  Information on hospitalizations is more readily available and linkable to these ambulatory systems.  These data are valuable to help refine a cohort, or track important clinical parameters over time, without the need for additional patient contact to assess his status and to learn, only at the next scheduled research visit, that a patient had been hospitalized and had his medical regimen altered.