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The Promise of Electronic Records: Around the Corner or Down the Road?

The Promise of Electronic Records: Around the Corner or Down the Road? In 2009, the US Congress passed the Health Information Technology for Economic and Clinical Health (HITECH) Act, which offers nearly $30 billion in financial incentives to physicians and hospitals that adopt and choose to meaningfully use electronic health records (EHRs).1 The act is meant to help a health care system that consumes $2.5 trillion each year and produces health care that is below the standards of safety, quality, and efficiency that should be expected in the United States. There is broad consensus among US policy makers that EHRs will play a key role in transforming health care into a safer, more effective, and more efficient system. Despite the promise of EHRs (often referred to as electronic medical records or EMRs), recent data on their benefits have been disappointing. Although studies have consistently shown that EHRs can help clinicians adhere to guideline-based care and reduce medication errors,2,3 beyond these narrow benefits, there is little evidence that EHRs improve patient outcomes and even less evidence that they improve the efficiency of care.4 The lackluster data on the benefits of EHRs have led to a marketplace where EHR adoption has been underwhelming: based on the latest estimates, only a third of ambulatory care physicians5 and an even smaller minority of US hospitals are using EHRs6 (broadly defined as electronic systems that incorporate electronic prescribing, clinical notes, results management, and basic clinical decision support).7 Because of the slow adoption of EHRs, the US Congress included incentives in HITECH. In this sea of disappointing data about EHRs comes some good news. In an innovative study published in this week's JAMA, Murff and colleagues8 push beyond the traditional uses of the EHR by demonstrating that natural language processing, when applied to electronic data, can help clinicians track adverse events after surgery. To many readers, the topic may appear esoteric, but its significance should not be underestimated. Instead, these findings suggest that EHRs can transform health care delivery. Until now, much of the benefits from EHRs have appeared to come from decision support capabilities,9 such as offering advice on avoiding 2 drugs with serious drug-drug interactions.3 Decision support is essentially a set of rules applied to structured data such as laboratory test results or a list of active medications. These rule-based capabilities are low-hanging fruit because they rely on what electronic systems do best—store and run algorithms on structured data. Yet there is so much more that EHRs could and should be able to do. Electronic health records will create greater value for clinicians when they allow clinicians and quality managers to reliably identify adverse events and track them over time. Their value as quality measurement tools will improve substantially when EHRs can automatically generate quality measures that account for the reasons guideline-driven care is adhered to or, if not, why not. Currently, few EHR systems can do these things reliably, primarily because much of the required information resides in “unstructured” form within clinicians' notes. These notes are rich in detail about signs and symptoms of patients' conditions, their priorities for clinical care, and their willingness to take some medications but not others. The notes often offer insights into why the clinician chose one medication over another, how patients responded to treatment, and other specifics key to understanding the care patients receive. Clinical notes have to be read manually to extract these details, which limits the ability of clinicians or researchers to examine large numbers of clinical encounters quickly and efficiently. Natural language processing has the potential to alter the landscape by analyzing the context of words and phrases in medical records making them available for computer processing, resulting in the ability to automatically interpret EHRs. Although no consensus definition of natural language processing exists, it is widely used to describe a field of computational linguistics that allows computers to understand human language. Natural language processing has been pursued for half a century, and although it is used in other industries (such as virtual customer services representatives at online retailers), applications in medicine have been limited. Natural language processing is used for processing diagnostic result reports such as those from radiological tests. Despite being in free-text form, these reports are highly structured and therefore easier for computer systems to process than unstructured physician and nursing notes. Unstructured notes are more idiosyncratic and their analysis remains a daunting task. The study by Murff et al,8 conducted at 6 Department of Veterans Affairs (VA) hospitals, offers hope that these types of documents may be amenable to computer processing. The VA has had a well-functioning EHR for almost 3 decades. It also has a robust research program that funds studies to improve the functioning of its EHR. The VA provides an ideal environment for developing and testing algorithms for identifying adverse events. Using natural language process, Murff et al8 assessed the records of 2974 surgical patients whose records were reviewed by trained nurses to detect complications. The natural language process algorithms had variable abilities to automatically detect complications with sensitivity ranging from 59% for venous thromboembolism to 91% for myocardial infarction. Compared with patient safety indicators that rely on billing data,10 natural language process demonstrated better sensitivity for detecting complications but only marginally worse specificity (knowing that a complication was truly absent when the algorithm said it was not present). Although patient safety indicators are commonly used, they generally have low sensitivity and poor specificity.11 The real test of natural language process will be whether it can become far more sensitive at identifying adverse events without losing its specificity. Murff et al showed a variety of approaches to identifying complications demonstrating that the sensitivity and specificity for finding them in an EHR are highly dependent on the algorithm used. Although the promise of natural language process is substantial, its benefits will not be realized without considerable new investment in research and development. Murff and colleagues focused on one specific application of identifying adverse events after surgery. Dozens of permutations and combinations of syntax were tested and customized to identify the optimal strategy for finding complications in an EHR. To realize the benefits of natural language process, this kind of research will need further development not only to find better algorithms but also to investigate EHR analysis for disciplines other than surgery and optimize automated EHR searches for different types of clinicians. Although there are private-sector companies capitalizing on the benefits of natural language process to help clinicians and organizations improve care delivery, the federal government can play a helpful role by funding the basic research needed to launch this field forward. US health care is entering a new era—moving from a cottage industry to one in which health care systems will be key to delivering safe, effective, and efficient care. Electronic health records will play a central role in this transformation. Currently, the EHR remains a tool with vast potential but a limited set of current capabilities. Natural language process has the potential for many new applications such as automated quality assessment to assisting in the performance of comparative effectiveness research. The promise of natural language process has been on the horizon for some time. The study by Murff et al suggests that these benefits may be closer than ever, but only if the power of computing is harnessed to understand the vast amount of written data that currently needs a pair of eyes and a human brain to comprehend. Back to top Article Information Corresponding Author: Ashish K. Jha, MD, MPH, Department of Health Policy and Management, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115 (ajha@hsph.harvard.edu). Conflict of Interest Disclosures: Dr Jha has completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and reported that he serves on the scientific advisory board of Humedica Inc, which pools clinical data to provide clinical intelligence to physicians and hospitals. Editorials represent the opinions of the authors and JAMA and not those of the American Medical Association. References 1. American Recovery and Reinvestment Act of 2009, HR 1, 111th Cong, 1st Sess (2009) 2. Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280(15):1311-13169794308PubMedGoogle ScholarCrossref 3. Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;163(12):1409-141612824090PubMedGoogle ScholarCrossref 4. DesRoches CM, Campbell EG, Vogeli C, et al. Electronic health records' limited successes suggest more targeted uses. Health Aff (Millwood). 2010;29(4):639-64620368593PubMedGoogle ScholarCrossref 5. Hsiao C-J, Beatty PC, Hing ES, Woodwell DA, Rechtsteiner EA, Sisk JE. Electronic Medical Record/Electronic Health Record Use by Office-based Physicians: United States, 2008 and Preliminary 2009. Hyattsville, MD: National Center for Health Statistics, Division of Health Care Statistics; 2010 6. Jha AK, DesRoches CM, Kralovec PD, Joshi MS. A progress report on electronic health records in US hospitals. Health Aff (Millwood). 2010;29(10):1951-195720798168PubMedGoogle ScholarCrossref 7. Jha AK, Ferris TG, Donelan K, et al. How common are electronic health records in the United States? a summary of the evidence. Health Aff (Millwood). 2006;25(6):w496-w50717035341PubMedGoogle ScholarCrossref 8. Murff HJ, FitzHenry F, Matheny ME, et al. Automated identification of post-operative complications within an electronic medical record using natural language processing. JAMA. 2011;306(8):848-85515383520PubMedGoogle ScholarCrossref 9. Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10):742-75216702590PubMedGoogle ScholarCrossref 10. McDonald KM, Romano PS.University of California San Francisco-Stanford Evidence-Based Practice Center. United States Agency for Healthcare Research and Quality: Measures of Patient Safety Based on Hospital Administrative Data—the Patient Safety Indicators. Rockville, MD: US Dept of Health and Human Services, Public Health Service, Agency for Healthcare Research and Quality; 2002 11. Romano PS, Mull HJ, Rivard PE, et al. Validity of selected AHRQ patient safety indicators based on VA National Surgical Quality Improvement Program data. Health Serv Res. 2009;44(1):182-20418823449PubMedGoogle ScholarCrossref http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA American Medical Association

The Promise of Electronic Records: Around the Corner or Down the Road?

JAMA , Volume 306 (8) – Aug 24, 2011

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References (12)

Publisher
American Medical Association
Copyright
Copyright © 2011 American Medical Association. All Rights Reserved.
ISSN
0098-7484
eISSN
1538-3598
DOI
10.1001/jama.2011.1219
Publisher site
See Article on Publisher Site

Abstract

In 2009, the US Congress passed the Health Information Technology for Economic and Clinical Health (HITECH) Act, which offers nearly $30 billion in financial incentives to physicians and hospitals that adopt and choose to meaningfully use electronic health records (EHRs).1 The act is meant to help a health care system that consumes $2.5 trillion each year and produces health care that is below the standards of safety, quality, and efficiency that should be expected in the United States. There is broad consensus among US policy makers that EHRs will play a key role in transforming health care into a safer, more effective, and more efficient system. Despite the promise of EHRs (often referred to as electronic medical records or EMRs), recent data on their benefits have been disappointing. Although studies have consistently shown that EHRs can help clinicians adhere to guideline-based care and reduce medication errors,2,3 beyond these narrow benefits, there is little evidence that EHRs improve patient outcomes and even less evidence that they improve the efficiency of care.4 The lackluster data on the benefits of EHRs have led to a marketplace where EHR adoption has been underwhelming: based on the latest estimates, only a third of ambulatory care physicians5 and an even smaller minority of US hospitals are using EHRs6 (broadly defined as electronic systems that incorporate electronic prescribing, clinical notes, results management, and basic clinical decision support).7 Because of the slow adoption of EHRs, the US Congress included incentives in HITECH. In this sea of disappointing data about EHRs comes some good news. In an innovative study published in this week's JAMA, Murff and colleagues8 push beyond the traditional uses of the EHR by demonstrating that natural language processing, when applied to electronic data, can help clinicians track adverse events after surgery. To many readers, the topic may appear esoteric, but its significance should not be underestimated. Instead, these findings suggest that EHRs can transform health care delivery. Until now, much of the benefits from EHRs have appeared to come from decision support capabilities,9 such as offering advice on avoiding 2 drugs with serious drug-drug interactions.3 Decision support is essentially a set of rules applied to structured data such as laboratory test results or a list of active medications. These rule-based capabilities are low-hanging fruit because they rely on what electronic systems do best—store and run algorithms on structured data. Yet there is so much more that EHRs could and should be able to do. Electronic health records will create greater value for clinicians when they allow clinicians and quality managers to reliably identify adverse events and track them over time. Their value as quality measurement tools will improve substantially when EHRs can automatically generate quality measures that account for the reasons guideline-driven care is adhered to or, if not, why not. Currently, few EHR systems can do these things reliably, primarily because much of the required information resides in “unstructured” form within clinicians' notes. These notes are rich in detail about signs and symptoms of patients' conditions, their priorities for clinical care, and their willingness to take some medications but not others. The notes often offer insights into why the clinician chose one medication over another, how patients responded to treatment, and other specifics key to understanding the care patients receive. Clinical notes have to be read manually to extract these details, which limits the ability of clinicians or researchers to examine large numbers of clinical encounters quickly and efficiently. Natural language processing has the potential to alter the landscape by analyzing the context of words and phrases in medical records making them available for computer processing, resulting in the ability to automatically interpret EHRs. Although no consensus definition of natural language processing exists, it is widely used to describe a field of computational linguistics that allows computers to understand human language. Natural language processing has been pursued for half a century, and although it is used in other industries (such as virtual customer services representatives at online retailers), applications in medicine have been limited. Natural language processing is used for processing diagnostic result reports such as those from radiological tests. Despite being in free-text form, these reports are highly structured and therefore easier for computer systems to process than unstructured physician and nursing notes. Unstructured notes are more idiosyncratic and their analysis remains a daunting task. The study by Murff et al,8 conducted at 6 Department of Veterans Affairs (VA) hospitals, offers hope that these types of documents may be amenable to computer processing. The VA has had a well-functioning EHR for almost 3 decades. It also has a robust research program that funds studies to improve the functioning of its EHR. The VA provides an ideal environment for developing and testing algorithms for identifying adverse events. Using natural language process, Murff et al8 assessed the records of 2974 surgical patients whose records were reviewed by trained nurses to detect complications. The natural language process algorithms had variable abilities to automatically detect complications with sensitivity ranging from 59% for venous thromboembolism to 91% for myocardial infarction. Compared with patient safety indicators that rely on billing data,10 natural language process demonstrated better sensitivity for detecting complications but only marginally worse specificity (knowing that a complication was truly absent when the algorithm said it was not present). Although patient safety indicators are commonly used, they generally have low sensitivity and poor specificity.11 The real test of natural language process will be whether it can become far more sensitive at identifying adverse events without losing its specificity. Murff et al showed a variety of approaches to identifying complications demonstrating that the sensitivity and specificity for finding them in an EHR are highly dependent on the algorithm used. Although the promise of natural language process is substantial, its benefits will not be realized without considerable new investment in research and development. Murff and colleagues focused on one specific application of identifying adverse events after surgery. Dozens of permutations and combinations of syntax were tested and customized to identify the optimal strategy for finding complications in an EHR. To realize the benefits of natural language process, this kind of research will need further development not only to find better algorithms but also to investigate EHR analysis for disciplines other than surgery and optimize automated EHR searches for different types of clinicians. Although there are private-sector companies capitalizing on the benefits of natural language process to help clinicians and organizations improve care delivery, the federal government can play a helpful role by funding the basic research needed to launch this field forward. US health care is entering a new era—moving from a cottage industry to one in which health care systems will be key to delivering safe, effective, and efficient care. Electronic health records will play a central role in this transformation. Currently, the EHR remains a tool with vast potential but a limited set of current capabilities. Natural language process has the potential for many new applications such as automated quality assessment to assisting in the performance of comparative effectiveness research. The promise of natural language process has been on the horizon for some time. The study by Murff et al suggests that these benefits may be closer than ever, but only if the power of computing is harnessed to understand the vast amount of written data that currently needs a pair of eyes and a human brain to comprehend. Back to top Article Information Corresponding Author: Ashish K. Jha, MD, MPH, Department of Health Policy and Management, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115 (ajha@hsph.harvard.edu). Conflict of Interest Disclosures: Dr Jha has completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and reported that he serves on the scientific advisory board of Humedica Inc, which pools clinical data to provide clinical intelligence to physicians and hospitals. Editorials represent the opinions of the authors and JAMA and not those of the American Medical Association. References 1. American Recovery and Reinvestment Act of 2009, HR 1, 111th Cong, 1st Sess (2009) 2. Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280(15):1311-13169794308PubMedGoogle ScholarCrossref 3. Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;163(12):1409-141612824090PubMedGoogle ScholarCrossref 4. DesRoches CM, Campbell EG, Vogeli C, et al. Electronic health records' limited successes suggest more targeted uses. Health Aff (Millwood). 2010;29(4):639-64620368593PubMedGoogle ScholarCrossref 5. Hsiao C-J, Beatty PC, Hing ES, Woodwell DA, Rechtsteiner EA, Sisk JE. Electronic Medical Record/Electronic Health Record Use by Office-based Physicians: United States, 2008 and Preliminary 2009. Hyattsville, MD: National Center for Health Statistics, Division of Health Care Statistics; 2010 6. Jha AK, DesRoches CM, Kralovec PD, Joshi MS. A progress report on electronic health records in US hospitals. Health Aff (Millwood). 2010;29(10):1951-195720798168PubMedGoogle ScholarCrossref 7. Jha AK, Ferris TG, Donelan K, et al. How common are electronic health records in the United States? a summary of the evidence. Health Aff (Millwood). 2006;25(6):w496-w50717035341PubMedGoogle ScholarCrossref 8. Murff HJ, FitzHenry F, Matheny ME, et al. Automated identification of post-operative complications within an electronic medical record using natural language processing. JAMA. 2011;306(8):848-85515383520PubMedGoogle ScholarCrossref 9. Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10):742-75216702590PubMedGoogle ScholarCrossref 10. McDonald KM, Romano PS.University of California San Francisco-Stanford Evidence-Based Practice Center. United States Agency for Healthcare Research and Quality: Measures of Patient Safety Based on Hospital Administrative Data—the Patient Safety Indicators. Rockville, MD: US Dept of Health and Human Services, Public Health Service, Agency for Healthcare Research and Quality; 2002 11. Romano PS, Mull HJ, Rivard PE, et al. Validity of selected AHRQ patient safety indicators based on VA National Surgical Quality Improvement Program data. Health Serv Res. 2009;44(1):182-20418823449PubMedGoogle ScholarCrossref

Journal

JAMAAmerican Medical Association

Published: Aug 24, 2011

Keywords: surgical procedures, operative,electronic medical records,adverse event,natural language processing,computers

There are no references for this article.