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• "Compared with the manual review of electronic charts, EHR-derived [structured data] measures can undercount practice performance, with a disproportionately negative impact on the number of patients captured as receiving a clinical preventive service or meeting a recommended treatment goal."
  
  "Diagnostic orders or results for mammogram had the lowest proportion (10.7%) of data recorded in structured fields recognized for automated quality measurement. The majority of the information for breast cancer screening was found as scanned patient documents and diagnostic imaging; both sources of information are not amenable for automated electronic queries."
  
  Source: Parsons A, McCullough C, Wang J, Shih S. Validity of electronic health record-derived quality measurement for performance monitoring. J Am Med Inform Assoc. 2012;19(4):604–609. doi:10.1136/amiajnl-2011-000557. (link)

   

• "Potential cases may be missed or wrongly dated when coded data alone are used to identify patients with [rheumatoid arthritis], as diagnostic suspicions are frequently confined to text. The use of EHRs to create disease registers or assess quality of care will be misleading if free text information is not taken into account."
  
  Source: Ford E, Nicholson A, Koeling R, et al. Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text?. BMC Med Res Methodol. 2013;13:105. Published 2013 Aug 21. doi:10.1186/1471-2288-13-105 (link)

   

• "In this paper, we demonstrate that information extraction from unstructured clinical narratives is essential to most clinical applications. We perform an empirical study to validate the argument and show that structured data alone is insufficient in resolving eligibility criteria for recruiting patients onto clinical trials for chronic lymphocytic leukemia (CLL) and prostate cancer. Unstructured data is essential to solving 59% of the CLL trial criteria and 77% of the prostate cancer trial criteria."
  
  "Structured data is best used for first pass filtering of EHR data...prior to a more nuanced second pass using unstructured data"
  
  Source: Raghavan P, Chen JL, Fosler-Lussier E, Lai AM. How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?. AMIA Jt Summits Transl Sci Proc. 2014;2014:218‐223. Published 2014 Apr 7. (link)

   

• "Urinary incontinence, an important patient-centered outcome following prostate cancer treatment, was reported almost exclusively in the free text of EHRs and was rarely coded as an ICD 9 diagnosis code."
  
  Source: Hernandez-Boussard T, Tamang S, Blayney D, Brooks J, Shah N. New Paradigms for Patient-Centered Outcomes Research in Electronic Medical Records: An Example of Detecting Urinary Incontinence Following Prostatectomy. EGEMS (Wash DC). 2016;4(3):1231. Published 2016 May 12. doi:10.13063/2327-9214.1231 (link)

   

• "Claims and structured EHR data give an incomplete picture of burden related to geriatric syndromes. Geriatric syndromes are likely to be missed if unstructured data are not analyzed."
  
  Source: Kharrazi H, Anzaldi LJ, Hernandez L, et al. The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification. J Am Geriatr Soc. 2018;66(8):1499‐1507. doi:10.1111/jgs.15411 (link)

   

• "There was variation by center, from 90% patients with complete agreement between the medication list and clinical narrative to 50% agreement...Our findings have important implications for deciding whether to solely rely on medication order data as a proxy for medication usage in research..."
  
  Source: Walsh KE, Marsolo KA, Davis C, et al. Accuracy of the medication list in the electronic health record-implications for care, research, and improvement. J Am Med Inform Assoc. 2018;25(7):909‐912. doi:10.1093/jamia/ocy027 (link)

   

• "Almost 1 in 4 medications were mismatched between the progress note and formal medication list in the EHR."
  
  Source: Ashfaq HA, Lester CA, Ballouz D, Errickson J, Woodward MA. Medication Accuracy in Electronic Health Records for Microbial Keratitis [published online ahead of print, 2019 May 30]. JAMA Ophthalmol. 2019;137(8):929‐931. doi:10.1001/jamaophthalmol.2019.1444 (link)

   

• "Family history information was frequently found in note narrative text rather than in the EHR family history tool"
  
  Source: Payne TH, Zhao LP, Le C, et al. Electronic health records contain dispersed risk factor information that could be used to prevent breast and ovarian cancer [published online ahead of print, 2020 Sep 17]. J Am Med Inform Assoc. 2020;ocaa152. doi:10.1093/jamia/ocaa152 (link)

   

• "All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD-9 codes and structured EHR data to 16.0 percent."
  
  Source: Navathe AS, Zhong F, Lei VJ, Chang FY, Sordo M, Topaz M, Navathe SB, Rocha RA, Zhou L. Hospital Readmission and Social Risk Factors Identified from Physician Notes. Health Serv Res. 2018 Apr;53(2):1110-1136. doi: 10.1111/1475-6773.12670. Epub 2017 Mar 13. PMID: 28295260; PMCID: PMC5867172. (link)

   

• "We identified over 50 individuals with two rare epilepsies that do not have specific ICD-10 codes for cohort creation (epilepsy with myoclonic atonic seizures, Sturge–Weber syndrome). Significance: Keyword search is an effective method for cohort creation. These findings can improve identification and surveillance of individuals with rare epi- lepsies and promote their referral to specialty clinics, clinical research, and support groups."
  
  Source: Barbour K, Tian N, Yozawitz EG, Wolf S, McGoldrick PE, Sands TT, Nelson A, Basma N, Grinspan ZM. Creating rare epilepsy cohorts using keyword search in electronic health records. Epilepsia. 2023 Jul 27. doi: 10.1111/epi.17725. Epub ahead of print. PMID: 37498137. (link)

   

• "Using unstructured data of patients with diabetes via EMERSE, we discovered more patient information related to a set of SDOH than we identified using structured data alone."
  
  Source: Mehta S, Lyles CR, Rubinsky AD, Kemper KE, Auerbach J, Sarkar U, Gottlieb L, Brown Iii W. Social Determinants of Health Documentation in Structured and Unstructured Clinical Data of Patients With Diabetes: Comparative Analysis. JMIR Med Inform. 2023 Aug 22;11:e46159. doi: 10.2196/46159. PMID: 37621203; PMCID: PMC10466443. (link)