1. Primary and Secondary Use of Clinical Data from the Electronic Health Record
BDK10-1
Secondary Use (Re-Use) of Clinical Information
William Hersh, MD
Department of Medical Informatics & Clinical Epidemiology
Oregon Health & Science University

2. Primary and Secondary Use of Clinical Data from the EHR
Information-related problems and solutions in healthcare
Opportunities for secondary use or re-use of clinical data for research and other purposes
Healthcare quality measurement and improvement

3. Many problems in healthcare have information-related solutions
Quality – not as good as it could be (McGlynn, 2003; Schoen, 2009; NCQA, 2010)
Safety – errors cause morbidity and mortality; many preventable (Kohn, 2000; Classen, 2011; van den Bos, 2011; Smith 2012)
Cost – rising costs not sustainable; US spends more but gets less (Angrisano, 2007; Brill, 2013)
Inaccessible information – missing information frequently in primary care (Smith, 2005)

4. Growing evidence that information interventions are part of solution
Systematic reviews (Chaudhry, 2006; Goldzweig, 2009; Buntin, 2011; Jones, 2014) have identified benefits in a variety of areas, although
Quality of many studies could be better
Large number of early studies came from a small number of “health IT leader” institutions
(Buntin, 2011)

5. What are the major challenges in getting where we want? (Hersh, 2004)
Cost
Technical challenges
Interoperability
Privacy and confidentiality
Workforce

6. US has made substantial investment in health information technology (HIT)
“To improve the quality of our health care while lowering its cost, we will make the immediate investments necessary to ensure that within five years, all of America’s medical records are computerized … It just won’t save billions of dollars and thousands of jobs – it will save lives by reducing the deadly but preventable medical errors that pervade our health care system.”
January 5, 2009
Health Information Technology for Economic and Clinical Health (HITECH) Act of the American Recovery and Reinvestment Act (ARRA) (Blumenthal, 2011)
Incentives for electronic health record (EHR) adoption by physicians and hospitals (up to $27B)
Direct grants administered by federal agencies ($2B)

7. Which has led to significant EHR adoption in the US
Office-based physicians
(Hsiao, 2014)
Emergency departments
(Jamoom, 2015)
Outpatient departments
(Jamoom, 2015)
Non-federal hospitals
(Charles, 2014)

8. Providing opportunities for “secondary use” or “re-use” of clinical data
(Safran, 2007; Rea, 2012)
Using data to improve care delivery
Healthcare quality measurement and improvement
Clinical and translational research
Public health surveillance
Implementing the learning health system

9. EHR and associated terminology
Electronic health record (EHR) – patient’s health record in digital form
Personal health record (PHR) – personally controlled health record
Claims data – data associated with administration and billing
International Classification of Diseases (ICD) – coding system for diagnoses, required for reimbursement
ICD-9-CM – used for decades until 2015, problems in depth and how it is used
ICD-10-CM – transition in 2015, may be excessively granular

10. Using data to improve healthcare
With shift of payment from “volume to value,” healthcare organizations will need to manage information better to provide better care (Diamond, 2009; Horner, 2012)
Predictive analytics is use of data to anticipate poor outcomes or increased resource use – applied by many to problem of early hospital re-admission (e.g., Gildersleeve, 2013; Amarasingham, 2013; Herbert, 2014; Shadmi, 2015)
A requirement for “precision medicine” (IOM, 2011; Collins, 2015)
Also can be used to measure quality of care delivered to make it more “accountable” (Hussey, 2013; Barkhuysen, 2014)

11. Clinical and translational research
Many roles for clinical research informatics (Richesson, 2012)
Led in part by activities of NIH Clinical and Translational Science Award (CTSA) Program (Mackenzie, 2012)
One of largest and most productive efforts has been eMERGE Network – connecting genotype-phenotype (Gottesman, 2013; Newton, 2013)
Has used EHR data to identify genomic variants associated with various phenotypes (Denny, 2012; Denny, 2013)

12. Clinical and translational research (cont.)
Other successes include replication of clinical studies, e.g.,
Randomized controlled trials (RCT)
Women’s Health Initiative (Tannen, 2007; Weiner, 2008)
Other cardiovascular diseases (Tannen, 2008; Tannen, 2009) and value of statin drugs in primary prevention of coronary heart disease (Danaei, 2011)
Observational studies
Metformin and reduced cancer mortality rate (Xu, 2014)
Much potential for using propensity scores with observational studies as complement to RCTs
Often but not always obtain same results as RCTs (Dahabreh, 2014)

13. Healthcare quality measurement and improvement
What is healthcare quality? From Blumenthal (1996)
Donabedian, 1988: “That kind of care which is expected to maximize an inclusive measure of patient welfare, after one has taken account of the balance of expected gains and losses that attend the process of care in all its parts.”
Lohr, IOM, 1990: The “degree to which health services for individuals and populations increase the likelihood of desired outcomes and are consistent with current professional knowledge.”
In era of rising costs and concerns about quality, physicians and the healthcare system must have public accountability (Chassin, 2010)

14. Why do we want to measure and improve quality?
The Berwick “triple aim” (Berwick, 2008)
Improving experience of care
Improving health of population
Reducing costs
Healthcare must strive to be “high-value, cost-conscious” (Owens, 2011), and “high reliability” (Chassin, 2011)
Must enable the “learning health system” (Friedman, 2010), allowing us to take advantage of data sources that already exist, such as administrative sources, registries, and electronic health record (EHR) data (Smith, 2012)

15. Donabedian (2002) model of quality
Three categories
Structural – factors that make it easier or harder to deliver high-quality care, e.g., hospital volume, physician licensure, nurse staffing levels
Process – factors describing healthcare content and activities, e.g., adherence to guidelines for screening, treatment, etc.
Outcomes – changes attributable to care, e.g., mortality, morbidity, functional status
Implemented and measured at different levels at an institution, e.g., individual, department, organization
In general, want to focus on outcomes
Represents what actually happens to patient
But difficult to measure and have confounding factors, so process measures are more commonly used

16. Ideal quality measures
“You can’t improve what you don’t measure”
Various
“In God we trust, all others bring data”
Edward Deming, statistician ( )
Landon (2003) attributes of ideal quality measures
Evidence-based
Agreed-on standards for satisfactory performance
Standardized specifications
Adequate sample size for reliable estimates
Adjustment for confounding patient factors
Care attributable to individual physician
Feasible to collect
Representative of activities of specialty

17. Finding quality measures
National Quality Forum (NQF)
Agency for Healthcare Research & Quality (AHRQ)

18. Finding quality measures – NQF

19. Diabetes mellitus (DM) and quality of care
Many process measures, e.g.,
Measurement of HbA1C
Control of lipids and blood pressure
Eye and foot exams
Etc.
Connection between process to outcome not always definitive (Pogach, 2011)

20. Measuring quality of DM care

21. A well-known measure is HbA1C

22. Another measure is of HbA1C level

23. Issues with DM and quality measures
Conventional wisdom was that tighter control of blood sugar (HbA1C < 7), blood pressure (<130/80), lipids (LDL-C < 100), etc. was better in Type 2 DM
Several RCTs showed otherwise
ACCORD trial prematurely terminated due to excess deaths in intensive treatment (HbA1C, blood pressure) group (Ismail-Beigi, 2010; Cushman, 2010)
Led to modifications in quality measurements (Pogach, 2011)
DM also hard to determine definitively in EHRs (Wei, 2013)