Difference between revisions of "Secondary use of EMR"

From Clinfowiki
Jump to: navigation, search
Line 56: Line 56:
  
 
Submitted by Nivedita Kumar
 
Submitted by Nivedita Kumar
 +
 +
 +
 +
== Additional stuff==
 +
 +
The huge increase in coded health data generated by electronic medical records has an enormous potential to increase our ability to do clinical research.  Compared to traditional research methods, there are many potential benefits and detriments to secondary use of clinical data.
 +
 +
 +
==Limitations==
 +
Limitations of secondary use of clinical data are that of retrospective research, and which is inherently subject to many sources of bias and error.  With prospective study design, a research question is posed and the study designed to be able to accurately measure and analyze the data required to answer the question.  Inconsistencies of medical terminology are a recognized challenge to research validity (misclassification bias), and each research plan requires careful attention to the definitions necessary to answer the question.    The conditions to be studied, the treatments rendered, and the outcomes to measure are carefully defined.    Templates are designed to enhance accuracy and minimize missing data.  Potential sources of bias and confounding are considered and managed.
 +
 +
==Case study==
 +
 +
The studies of estrogen therapy after menopause are an excellent example of bias and erroneous findings in retrospective studies.  Briefly, before the Women’s Health Initiative (WHI) results were published 2002 (Rossouw JE, JAMA, 2002) (a randomized trial of estrogen therapy in menopausal women), there were numerous retrospective studies indicating that women who used menopausal hormone therapy had a 50% reduction in death from heart disease.    Women were encouraged to take estrogen by clinicians as a strategy for reducing heart disease.  A question posed by many researchers was “Did this finding occur because 1) estrogen improves cardiovascular function or  2) healthier women choose estrogen more often than less healthy women?” (selection bias).    The randomized trial found that estrogen did NOT confer a cardiac benefit, and now estrogen is NOT recommended as a strategy for reducing heart disease.    This story emphasizes the magnitude and impact of potential errors that may result from retrospective research. Note--this illustration is simplified, and does not represent the complexities of an individual woman’s benefit or risk of taking hormone therapy.)
 +
 +
Understanding the potential limitations of secondary use of data will facilitate changes to mitigate the risks.    Already there is much emphasis on improving the clarity of medical terminology.  Research questions can be “designed in” to EMR’s to accurately capture the data needed to answer the question, using templates, drop-down menus with definitions provided (research decision support).  Currently the risks of secondary use of data are large, but it is within the realm of EMR design to mitigate these risks.  When such design changes have been implemented, EMR’s will be able to provide a rich source of data for analysis for clinical research to enhance human health.
  
 
[[Category:BMI512-W-10]]
 
[[Category:BMI512-W-10]]

Revision as of 22:11, 20 October 2011

Secondary Use of EHR for Clinical Research

The Electronic Health Record (EHR) is the primary point of data capture for patient care. In addition to its primary purpose, EHRs can serve as data capture points for secondary uses such as clinical research. Secondary use of health data applies personal health information (PHI) for uses outside of direct health care delivery(1). EHR holds a great potential for supporting clinical research through improving efficiency, quality and reducing the cost of clinical trials. An optimal EHR can be developed to support research related activities including: clinical trials; comparative effectiveness quality measurement; and public health and safety monitoring, including post-marketing surveillance.


Advantages of secondary use health data for research purposes include but not limited to

  1. Observational and case series can be conducted quickly facilitating new research hypothesis for potential intervention.
  1. Without the assistance of Information technology , recruitment is extremely slow, expensive, and low-yield. EHR can also aid in expediting the clinical trials by allowing the available database to be used for screening potential subjects .
  1. Through data mining of data set the drug responses and potential toxicities, response to treatment long term sequeale and survival can be monitored for individual patients enrolled in the trials . Trough real time data collection clinical research , patient care and safety can be enhanced.


Challenges for secondary use of health data for clinical research

  1. Confidentiality, privacy, security, and data access

The most important concern is protecting the information from inappropriate use . This can be addressed through appropriate regulatory processes such as HIPPA and IRB approval/privacy board approval and modern security techniques such as access control encryption etc .

  1. Standardization EHR data for Research purposes.

Standardization of data increase data accuracy, availability and enable data integration. Several initiatives such as (RCRIM) Regulated clinical research Information Model through HL7 and non profit initiative through Clinical data Interchangeable standards Consortium (CDISC) are now collaborating together to develop final model of data standardization to meet the needs of clinical research .

  1. Improving the quality of data research .

As mentioned above data standardization is critical for improving quality of research. In addition to standardization, data collected in real time at the point of care by the primary care provider will ensure accurate and consistent data collection as opposed to retrospective reviews.


As noted in HIT Policy Committee report (2) the following three areas were projected to have a positive impact on meaningfull use of EHR for clinical research

  1. Supporting Opportunities for Patient Participation by :

Enabled interface can increasing patient interest and queries recarding the clinical trials thus doubling the enrollemnet in 3 years.

Incentives can reduce costs and recruitment time for federally sponsored trials.


  1. Reducing Barriers for Provider Participation in Clinical Research.

By providing incentives for meaningful use of EHR greater provider participation in clinical research can be assured.

  1. Use of Standards-based EHR Data for Clinical Research.

Use of standardized data can enhance efficiency and accuracy of data collected thus improving the quality of research

Thus by developing an optimal EHR capable of addressing the needs of clinical research with due consideration to privacy protection and data standardization will accelerate research and improve health care effectiveness , efficiency , reduce health care expenditure and enhance patient safety..

References

  1. Toward a National Framework for the Secondary Use of Health Data: An American Medical Informatics Association White Paper J Am Med Inform Assoc. 2007 Jan–Feb; 14(1): 1–9. Charles Safran, MD, MS, Meryl Bloomrosen, MBA, W. Edward Hammond, PHD, Steven Labkoff, MD, Suzanne Markel-Fox, PHD, Paul C. Tang, MD, Don E. Detmer, MD, MA,
  2. Designation of Clinical Research Information Integration as an Objective of“Meaningful Use” of Electronic Health Record Systems Presentation to the HIT Policy Committee Gregory Downing.

Submitted by Nivedita Kumar


Additional stuff

The huge increase in coded health data generated by electronic medical records has an enormous potential to increase our ability to do clinical research. Compared to traditional research methods, there are many potential benefits and detriments to secondary use of clinical data.


Limitations

Limitations of secondary use of clinical data are that of retrospective research, and which is inherently subject to many sources of bias and error. With prospective study design, a research question is posed and the study designed to be able to accurately measure and analyze the data required to answer the question. Inconsistencies of medical terminology are a recognized challenge to research validity (misclassification bias), and each research plan requires careful attention to the definitions necessary to answer the question. The conditions to be studied, the treatments rendered, and the outcomes to measure are carefully defined. Templates are designed to enhance accuracy and minimize missing data. Potential sources of bias and confounding are considered and managed.

Case study

The studies of estrogen therapy after menopause are an excellent example of bias and erroneous findings in retrospective studies. Briefly, before the Women’s Health Initiative (WHI) results were published 2002 (Rossouw JE, JAMA, 2002) (a randomized trial of estrogen therapy in menopausal women), there were numerous retrospective studies indicating that women who used menopausal hormone therapy had a 50% reduction in death from heart disease. Women were encouraged to take estrogen by clinicians as a strategy for reducing heart disease. A question posed by many researchers was “Did this finding occur because 1) estrogen improves cardiovascular function or 2) healthier women choose estrogen more often than less healthy women?” (selection bias). The randomized trial found that estrogen did NOT confer a cardiac benefit, and now estrogen is NOT recommended as a strategy for reducing heart disease. This story emphasizes the magnitude and impact of potential errors that may result from retrospective research. Note--this illustration is simplified, and does not represent the complexities of an individual woman’s benefit or risk of taking hormone therapy.)

Understanding the potential limitations of secondary use of data will facilitate changes to mitigate the risks. Already there is much emphasis on improving the clarity of medical terminology. Research questions can be “designed in” to EMR’s to accurately capture the data needed to answer the question, using templates, drop-down menus with definitions provided (research decision support). Currently the risks of secondary use of data are large, but it is within the realm of EMR design to mitigate these risks. When such design changes have been implemented, EMR’s will be able to provide a rich source of data for analysis for clinical research to enhance human health.