Accepted Manuscript

Can Natural Language Processing Fulfill the Promise of Electronic Medical Records?

Paul A. Heidenreich, MD, MS

PII: S1071-9164(14)00188-2

DOI: 10.1016/j.cardfail.2014.04.020

Reference: YJCAF 3296

To appear in: Journal of Cardiac Failure

Received Date: 28 April 2014

Accepted Date: 28 April 2014

Please cite this article as: Heidenreich PA, Can Natural Language Processing Fulfill the Promise ofElectronic Medical Records?, Journal of Cardiac Failure (2014), doi: 10.1016/j.cardfail.2014.04.020.

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Can Natural Language Processing Fulfill the Promise of Electronic Medical Records?

Paul A. Heidenreich MD, MS

Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, 94304

Paul Heidenreich MD, MS

VA Palo Alto HCS, 111C

3801 Miranda Avenue, Palo Alto, CA 94306

650-849-1205

Fax 650-852-3473

Email paul.heidenreich@va.gov

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Electronic medical records (EMRs) are now common in US health care settings though their

implementation has not always been welcomed by providers. [1] The promised large

improvements in efficiency and quality of care have not occurred, and many have argued that

such benefits will only be realized when provider notes are structured using check boxes or

drop down menus. While such a method of documenting will allow easier analysis, the effort

needed to enter all relevant data in this way is substantial and unattractive to clinicians.

However, such electronic data are already needed to report quality metrics to payers and

accrediting organizations, and facilities have resorted to hiring abstracters to collect data from

chart review. The cost of data abstraction by hand is high and markedly limits the ability of

facilities to participate in quality improvement activities, registries, and research collaborations.

A solution to this growing need for electronic data that limits the burden on providers and

hospitals is the use of natural language processing (NLP).

The process of NLP starts with unstructured (raw) text and determines the presence and

meaning of certain words or word combinations. [2] Neighboring words are also examined to

determine if they negate the finding such as “no” in “no edema”, or falsely negate the finding

such as the “no” in “no change in edema”. The accuracy is determined using trained chart

reviewers and revisions are made until an acceptable sensitivity (known as recall in NLP

terminology) and specificity are reached. Given the potential for such iterations to lead to

“overfitting” the optimized NLP algorithm is then tested in an independent dataset.

Prior work has demonstrated that NLP can be used to extract heart failure related findings from

radiography reports with high accuracy. In one study ,[2] NLP was found to be significantly

more accurate than hospital staff coders using expert review as the gold standard.

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Cardiomegaly was identified by NLP with a sensitivity of 96% and a specificity of 100%

compared to 91% (sensitivity) and 90% (specificity) for hospital staff coders. Pleural effusions

were also more reliably identified with NLP compared to hospital staff (sensitivity 98% vs 78%,

both with 100% specificity). The Veterans Affairs Health Care System has recently used NLP

to extract the left ventricular ejection fraction from echocardiography reports with a high degree

of accuracy. [3] The goal is to obtain LVEF values from all reports system wide, make these

available in fields within the medical record, and then use the data to monitor and improve

performance (e.g. identify potential candidates for beta-blockers or mineralocorticoid

antagonists).

The study by Steinhubl and colleagues in this issue of the Journal [4] shows the additional

ability of NLP to identify heart failure signs and symptoms, and demonstrates the potential for

early identification of patients with heart failure. The investigators used NLP to scan over

three years of provider’s notes for over 4000 incident heart failure cases to determine the time

from first mention of a sign or symptom to subsequent diagnosis of heart failure. By including

a control group without a diagnosis of heart failure they were able to determine the predictive

value of certain symptoms, signs and findings of heart failure. One provocative finding was

that paroxysmal nocturnal dyspnea was present in 41% of patients ultimately diagnosed with

heart failure and was noted almost two years before a heart failure diagnosis was made. One

can see many potential clinical uses for this technology in identifying potential patients with

heart failure. Simple lists of patients with possible heart failure can be provided to providers in

the patient centered medical home. More sophisticated decision support can prompt clinicians

to consider certain diagnoses, order tests (e.g. echocardiography), or initiate treatment.

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For patients with established heart failure, NLP has the potential to allow us to complement the

current referral system of specialty care with one of population health where proactive

involvement in patient care can occur through assessment of existing data. NLP can occur in

real time providing a continuous oversight of a population to identify patients who may benefit

from additional treatment including life prolonging medications and devices.

Once candidate patients are identified, referrals to specialists can be suggested and/or

computerized decision support can be made available to the primary provider.

The potential of NLP to reduce the cost of research and registry participation is also significant.

One of the greatest barriers to participation in research and clinical registries is the personnel

needed to collect data. NLP can drop this cost dramatically making it feasible for all hospitals

to participate in data collection collaborations (e.g. registries). Identification of potential

research subjects will be much faster with NLP and the ability to easily extract baseline data

and outcomes will allow pragmatic trials for a large number of conditions.

Another area of potential benefit of NLP in heart failure is improved prediction of

decompensation. Identification of readmissions is of particular interest to US hospitals as they

face large financial penalties for high readmission rates. Unfortunately, available models for

predicting readmission using data available from existing electronic records perform poorly (c

statistics < 0.65). Studies suggest that additional data on social status can improve prediction

of admission [5] and NLP can be helpful in capturing this data that often appears only in text.

Unfortunately, there are still significant barriers to developing and using NLP. Large upfront

personnel costs are needed to produce NLP algorithms. Expert chart reviews are required to

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create the gold standard against which NLP is tested and most facilities will not have the ability

to develop these. Not all symptoms, signs and diagnoses can be accurately captured from

existing notes and separate NLP algorithms will need to be developed and tested for each data

element. There is also a lack of standardization. It is usually difficult to adapt an NLP that

has been developed by an outside institution given the variety of software used and the

differences in electronic medical records. Finally, NLP is only as good as the provider

documentation on which it is based. Any omissions or inaccuracies in provider notes will be

incorporated into the NLP results.

In summary, the electronic medical record contains a wealth of medical information as

narrative text which is difficult to find and use for clinical care and research. Extracting this

information from the text document has traditionally required manual chart review which is time

consuming and expensive. The study by Steinhubl using NLP suggests how the promised

economic and health benefits of electronic medical records may yet be achieved.

Disclosures: None

References

1) Babbott S, Manwell LB, Brown R, Montague E, Williams E, Schwartz M, Hess E,Linzer

M. Electronic medical records and physician stress in primary care: results from the

MEMO Study. J Am Med Inform Assoc. 2014 Feb;21(e1):e100-6. doi: 10.1136/amiajnl-

2013-001875.

2) Friedlin J, McDonald CJ. A natural language processing system to extract and code

concepts relating to congestive heart failure from chest radiology reports. AMIA Annu

Symp Proc. 2006:269-73.

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3) Garvin JH, DuVall SL, South BR, Bray BE, Bolton D, Heavirland J, Pickard S,

Heidenreich P, Shen S, Weir C, Samore M, Goldstein MK. Automated extraction of

ejection fraction for quality measurement using regular expressions in Unstructured

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2012 Sep-Oct;19(5):859-66.

4) CURRENT STUDY: Steinhubl Prevalence of Heart Failure Signs and Symptoms in a

Large Primary Care Population Identified Through the Use of Text and Data Mining of

the Electronic Health Record

5) Calvillo-King L, Arnold D, Eubank KJ, Lo M, Yunyongying P, Stieglitz H, Halm EA.

Impact of social factors on risk of readmission or mortality in pneumonia and heart

failure: systematic review. J Gen Intern Med. 2013 Feb;28(2):269-82.