Adler-Milstein J, Holmgren AJ, Kralovec P, et al. Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide. J Am Med Inform Assoc. 2017;24(6):1142–8.
Article
PubMed
PubMed Central
Google Scholar
Office of the National Coordinator for Health Information Technology. ‘Office-based physician electronic health record adoption’, Health IT quick-stat #50. dashboard.healthit.gov/quickstats/pages/physician-ehr-adoption-trends.php. Accessed 15 Jan 2019.
Cowie MR, Blomster JI, Curtis LH, et al. Electronic health records to facilitate clinical research. Clin Res Cardiol. 2017;106(1):1–9.
Article
PubMed
Google Scholar
Casey JA, Schwartz BS, Stewart WF, et al. Using electronic health records for population health research: a review of methods and applications. Annu Rev Public Health. 2016;37:61–81.
Article
PubMed
Google Scholar
Verheij RA, Curcin V, Delaney BC, et al. Possible sources of bias in primary care electronic health record data use and reuse. J Med Internet Res. 2018;20(5):e185.
Article
PubMed
PubMed Central
Google Scholar
Ni K, Chu H, Zeng L, et al. Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study. BMJ Open. 2019;9(7):e029314.
Article
PubMed
PubMed Central
Google Scholar
Coleman N, Halas G, Peeler W, et al. From patient care to research: a validation study examining the factors contributing to data quality in a primary care electronic medical record database. BMC Fam Pract. 2015;16:11.
Article
PubMed
PubMed Central
Google Scholar
Kruse CS, Stein A, Thomas H, et al. The use of electronic health records to support population health: a systematic review of the literature. J Med Syst. 2018;42(11):214.
Article
PubMed
PubMed Central
Google Scholar
Shortreed SM, Cook AJ, Coley RY, et al. Challenges and opportunities for using big health care data to advance medical science and public health. Am J Epidemiol. 2019;188(5):851–61.
Article
PubMed
Google Scholar
In: Smedley BD, Stith AY, Nelson AR, editors. Unequal treatment: confronting racial and ethnic disparities in health care. Washington (DC) 2003.
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):742–52.
Article
PubMed
Google Scholar
Cutler DM, Scott Morton F. Hospitals, market share, and consolidation. JAMA. 2013;310(18):1964–70.
Article
CAS
PubMed
Google Scholar
Cocoros NM, Kirby C, Zambarano B, et al. RiskScape: a data visualization and aggregation platform for public health surveillance using routine electronic health record data. Am J Public Health. 2021;111(2):269–76.
Article
PubMed
Google Scholar
Vader DT, Weldie C, Welles SL, et al. Hospital-acquired Clostridioides difficile infection among patients at an urban safety-net hospital in Philadelphia: demographics, neighborhood deprivation, and the transferability of national statistics. Infect Control Hosp Epidemiol. 2020;42:1–7.
Google Scholar
Dixon BE, Gibson PJ, Frederickson Comer K, et al. Measuring population health using electronic health records: exploring biases and representativeness in a community health information exchange. Stud Health Technol Inform. 2015;216:1009.
PubMed
Google Scholar
Hernán MA, VanderWeele TJ. Compound treatments and transportability of causal inference. Epidemiology. 2011;22(3):368–77.
Article
PubMed
PubMed Central
Google Scholar
Casey JA, Pollak J, Glymour MM, et al. Measures of SES for electronic health record-based research. Am J Prev Med. 2018;54(3):430–9.
Article
PubMed
Google Scholar
Polubriaginof FCG, Ryan P, Salmasian H, et al. Challenges with quality of race and ethnicity data in observational databases. J Am Med Inform Assoc. 2019;26(8-9):730–6.
Article
PubMed
PubMed Central
Google Scholar
U.S. Census Bureau. Health. Available at: https://www.census.gov/topics/health.html. Accessed 19 Jan 2021.
Gianfrancesco MA, McCulloch CE, Trupin L, et al. Reweighting to address nonparticipation and missing data bias in a longitudinal electronic health record study. Ann Epidemiol. 2020;50:48–51 e2.
Article
PubMed
PubMed Central
Google Scholar
Goldstein ND, Kahal D, Testa K, Burstyn I. Inverse probability weighting for selection bias in a Delaware community health center electronic medical record study of community deprivation and hepatitis C prevalence. Ann Epidemiol. 2021;60:1–7.
Article
PubMed
Google Scholar
Gelman A, Lax J, Phillips J, et al. Using multilevel regression and poststratification to estimate dynamic public opinion. Unpublished manuscript, Columbia University. 2016 Sep 11. Available at: http://www.stat.columbia.edu/~gelman/research/unpublished/MRT(1).pdf. Accessed 22 Jan 2021.
Quick H, Terloyeva D, Wu Y, et al. Trends in tract-level prevalence of obesity in philadelphia by race-ethnicity, space, and time. Epidemiology. 2020;31(1):15–21.
Article
PubMed
Google Scholar
Lesko CR, Buchanan AL, Westreich D, Edwards JK, Hudgens MG, Cole SR. Generalizing study results: a potential outcomes perspective. Epidemiology. 2017;28(4):553–61.
Article
PubMed
PubMed Central
Google Scholar
Westreich D, Edwards JK, Lesko CR, Stuart E, Cole SR. Transportability of trial results using inverse odds of sampling weights. Am J Epidemiol. 2017;186(8):1010–4.
Article
PubMed
PubMed Central
Google Scholar
Congressional Research Services (CRS). The Health Information Technology for Economic and Clinical Health (HITECH) Act. 2009. Available at: https://crsreports.congress.gov/product/pdf/R/R40161/9. Accessed Jan 22 2021.
Google Scholar
Hersh WR. The electronic medical record: Promises and problems. Journal of the American Society for Information Science. 1995;46(10):772–6.
Article
Google Scholar
Collecting sexual orientation and gender identity data in electronic health records: workshop summary. Washington (DC) 2013.
Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records; Board on Population Health and Public Health Practice; Institute of Medicine. Capturing social and behavioral domains and measures in electronic health records: phase 2. Washington (DC): National Academies Press (US); 2015.
Goff SL, Pekow PS, Markenson G, et al. Validity of using ICD-9-CM codes to identify selected categories of obstetric complications, procedures and co-morbidities. Paediatr Perinat Epidemiol. 2012;26(5):421–9.
Article
PubMed
Google Scholar
Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005;58(4):323–37.
Article
PubMed
Google Scholar
Gianfrancesco MA. Application of text mining methods to identify lupus nephritis from electronic health records. Lupus Science & Medicine. 2019;6:A142.
Google Scholar
National Library of Medicine. SNOMED CT to ICD-10-CM Map. Available at: https://www.nlm.nih.gov/research/umls/mapping_projects/snomedct_to_icd10cm.html. Accessed 2 Jul 2021.
Klabunde CN, Harlan LC, Warren JL. Data sources for measuring comorbidity: a comparison of hospital records and medicare claims for cancer patients. Med Care. 2006;44(10):921–8.
Article
PubMed
Google Scholar
Burles K, Innes G, Senior K, Lang E, McRae A. Limitations of pulmonary embolism ICD-10 codes in emergency department administrative data: let the buyer beware. BMC Med Res Methodol. 2017;17(1):89.
Article
PubMed
PubMed Central
Google Scholar
Asgari MM, Wu JJ, Gelfand JM, Salman C, Curtis JR, Harrold LR, et al. Validity of diagnostic codes and prevalence of psoriasis and psoriatic arthritis in a managed care population, 1996-2009. Pharmacoepidemiol Drug Saf. 2013;22(8):842–9.
Article
PubMed
PubMed Central
Google Scholar
Hoffman S, Podgurski A. Big bad data: law, public health, and biomedical databases. J Law Med Ethics. 2013;41(Suppl 1):56–60.
Article
PubMed
Google Scholar
Adler-Milstein J, Jha AK. Electronic health records: the authors reply. Health Aff. 2014;33(10):1877.
Article
Google Scholar
Geruso M, Layton T. Upcoding: evidence from medicare on squishy risk adjustment. J Polit Econ. 2020;12(3):984–1026.
Article
PubMed
PubMed Central
Google Scholar
Lash TL, Fox MP, Fink AK. Applying quantitative bias analysis to epidemiologic data. New York: Springer-Verlag New York; 2009.
Book
Google Scholar
Gustafson P. Measurement error and misclassification in statistics and epidemiology: impacts and Bayesian adjustments. Boca Raton: Chapman and Hall/CRC; 2004.
Google Scholar
Duda SN, Shepherd BE, Gadd CS, et al. Measuring the quality of observational study data in an international HIV research network. PLoS One. 2012;7(4):e33908.
Article
CAS
PubMed
PubMed Central
Google Scholar
Shepherd BE, Yu C. Accounting for data errors discovered from an audit in multiple linear regression. Biometrics. 2011;67(3):1083–91.
Article
PubMed
PubMed Central
Google Scholar
Weiskopf NG, Hripcsak G, Swaminathan S, et al. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform. 2013;46(5):830–6.
Article
PubMed
Google Scholar
Kaiser Health News. As coronavirus strikes, crucial data in electronic health records hard to harvest. Available at: https://khn.org/news/as-coronavirus-strikes-crucial-data-in-electronic-health-records-hard-to-harvest/. Accessed 15 Jan 2021.
Reeves JJ, Hollandsworth HM, Torriani FJ, Taplitz R, Abeles S, Tai-Seale M, et al. Rapid response to COVID-19: health informatics support for outbreak management in an academic health system. J Am Med Inform Assoc. 2020;27(6):853–9.
Article
PubMed
PubMed Central
Google Scholar
Grange ES, Neil EJ, Stoffel M, Singh AP, Tseng E, Resco-Summers K, et al. Responding to COVID-19: The UW medicine information technology services experience. Appl Clin Inform. 2020;11(2):265–75.
Article
PubMed
PubMed Central
Google Scholar
Madigan D, Ryan PB, Schuemie M, et al. Evaluating the impact of database heterogeneity on observational study results. Am J Epidemiol. 2013;178(4):645–51.
Article
PubMed
PubMed Central
Google Scholar
Lippi G, Mattiuzzi C. Critical laboratory values communication: summary recommendations from available guidelines. Ann Transl Med. 2016;4(20):400.
Article
PubMed
PubMed Central
Google Scholar
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.
Article
CAS
PubMed
Google Scholar
Jones RN. Differential item functioning and its relevance to epidemiology. Curr Epidemiol Rep. 2019;6:174–83.
Article
PubMed
PubMed Central
Google Scholar
Edwards JK, Cole SR, Troester MA, Richardson DB. Accounting for misclassified outcomes in binary regression models using multiple imputation with internal validation data. Am J Epidemiol. 2013;177(9):904–12.
Article
PubMed
PubMed Central
Google Scholar
Satkunasivam R, Klaassen Z, Ravi B, Fok KH, Menser T, Kash B, et al. Relation between surgeon age and postoperative outcomes: a population-based cohort study. CMAJ. 2020;192(15):E385–92.
Article
PubMed
PubMed Central
Google Scholar
Melamed N, Asztalos E, Murphy K, Zaltz A, Redelmeier D, Shah BR, et al. Neurodevelopmental disorders among term infants exposed to antenatal corticosteroids during pregnancy: a population-based study. BMJ Open. 2019;9(9):e031197.
Article
PubMed
PubMed Central
Google Scholar
Kao LT, Lee HC, Lin HC, Tsai MC, Chung SD. Healthcare service utilization by patients with obstructive sleep apnea: a population-based study. PLoS One. 2015;10(9):e0137459.
Article
PubMed
PubMed Central
CAS
Google Scholar
Jung K, LePendu P, Iyer S, Bauer-Mehren A, Percha B, Shah NH. Functional evaluation of out-of-the-box text-mining tools for data-mining tasks. J Am Med Inform Assoc. 2015;22(1):121–31.
Article
PubMed
Google Scholar
Canan C, Polinski JM, Alexander GC, et al. Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review. J Am Med Inform Assoc. 2017;24(6):1204–10.
Article
PubMed
PubMed Central
Google Scholar
Iqbal E, Mallah R, Jackson RG, et al. Identification of adverse drug events from free text electronic patient records and information in a large mental health case register. PLoS One. 2015;10(8):e0134208.
Article
PubMed
PubMed Central
CAS
Google Scholar
Rochefort CM, Verma AD, Eguale T, et al. A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data. J Am Med Inform Assoc. 2015;22(1):155–65.
Article
PubMed
Google Scholar
Koleck TA, Dreisbach C, Bourne PE, et al. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J Am Med Inform Assoc. 2019;26(4):364–79.
Article
PubMed
PubMed Central
Google Scholar
Wang L, Luo L, Wang Y, et al. Natural language processing for populating lung cancer clinical research data. BMC Med Inform Decis Mak. 2019;19(Suppl 5):239.
Article
PubMed
PubMed Central
Google Scholar
Banerji A, Lai KH, Li Y, et al. Natural language processing combined with ICD-9-CM codes as a novel method to study the epidemiology of allergic drug reactions. J Allergy Clin Immunol Pract. 2020;8(3):1032–1038.e1.
Article
PubMed
Google Scholar
Zhang D, Yin C, Zeng J, et al. Combining structured and unstructured data for predictive models: a deep learning approach. BMC Med Inform Decis Mak. 2020;20(1):280.
Article
CAS
PubMed
PubMed Central
Google Scholar
Farmer R, Mathur R, Bhaskaran K, Eastwood SV, Chaturvedi N, Smeeth L. Promises and pitfalls of electronic health record analysis. Diabetologia. 2018;61:1241–8.
Article
PubMed
Google Scholar
Haneuse S, Arterburn D, Daniels MJ. Assessing missing data assumptions in EHR-based studies: a complex and underappreciated task. JAMA Netw Open. 2021;4(2):e210184.
Article
PubMed
Google Scholar
Groenwold RHH. Informative missingness in electronic health record systems: the curse of knowing. Diagn Progn Res. 2020;4:8.
Article
PubMed
PubMed Central
Google Scholar
Berkowitz SA, Traore CY, Singer DE, et al. Evaluating area-based socioeconomic status indicators for monitoring disparities within health care systems: results from a primary care network. Health Serv Res. 2015;50(2):398–417.
Article
PubMed
Google Scholar
Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible - the neighborhood atlas. N Engl J Med. 2018;378(26):2456–8.
Article
PubMed
PubMed Central
Google Scholar
Cantor MN, Thorpe L. Integrating data on social determinants of health into electronic health records. Health Aff. 2018;37(4):585–90.
Article
Google Scholar
Adler NE, Stead WW. Patients in context--EHR capture of social and behavioral determinants of health. N Engl J Med. 2015;372(8):698–701.
Article
CAS
PubMed
Google Scholar
Chen M, Tan X, Padman R. Social determinants of health in electronic health records and their impact on analysis and risk prediction: a systematic review. J Am Med Inform Assoc. 2020;27(11):1764–73.
Article
PubMed
PubMed Central
Google Scholar
Goldstein BA, Bhavsar NA, Phelan M, et al. Controlling for informed presence bias due to the number of health encounters in an electronic health record. Am J Epidemiol. 2016;184(11):847–55.
Article
PubMed
PubMed Central
Google Scholar
Petersen I, Welch CA, Nazareth I, et al. Health indicator recording in UK primary care electronic health records: key implications for handling missing data. Clin Epidemiol. 2019;11:157–67.
Article
PubMed
PubMed Central
Google Scholar
Li R, Chen Y, Moore JH. Integration of genetic and clinical information to improve imputation of data missing from electronic health records. J Am Med Inform Assoc. 2019;26(10):1056–63.
Article
PubMed
PubMed Central
Google Scholar
Koonin LM, Hoots B, Tsang CA, Leroy Z, Farris K, Jolly T, et al. Trends in the use of telehealth during the emergence of the COVID-19 pandemic - United States, January-March 2020. MMWR Morb Mortal Wkly Rep. 2020;69(43):1595–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Barnett ML, Ray KN, Souza J, Mehrotra A. Trends in telemedicine use in a large commercially insured population, 2005-2017. JAMA. 2018;320(20):2147–9.
Article
PubMed
PubMed Central
Google Scholar
Franklin JM, Gopalakrishnan C, Krumme AA, et al. The relative benefits of claims and electronic health record data for predicting medication adherence trajectory. Am Heart J. 2018;197:153–62.
Article
PubMed
Google Scholar
Devoe JE, Gold R, McIntire P, et al. Electronic health records vs Medicaid claims: completeness of diabetes preventive care data in community health centers. Ann Fam Med. 2011;9(4):351–8.
Article
PubMed
PubMed Central
Google Scholar
Schmajuk G, Li J, Evans M, Anastasiou C, Izadi Z, Kay JL, et al. RISE registry reveals potential gaps in medication safety for new users of biologics and targeted synthetic DMARDs. Semin Arthritis Rheum. 2020 Dec;50(6):1542–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Izadi Z, Schmajuk G, Gianfrancesco M, Subash M, Evans M, Trupin L, et al. Rheumatology Informatics System for Effectiveness (RISE) practices see significant gains in rheumatoid arthritis quality measures. Arthritis Care Res. 2020. https://doi.org/10.1002/acr.24444.
Angier H, Gold R, Gallia C, Casciato A, Tillotson CJ, Marino M, et al. Variation in outcomes of quality measurement by data source. Pediatrics. 2014;133(6):e1676–82.
Article
PubMed
Google Scholar
Lin KJ, Schneeweiss S. Considerations for the analysis of longitudinal electronic health records linked to claims data to study the effectiveness and safety of drugs. Clin Pharmacol Ther. 2016;100(2):147–59.
Article
CAS
PubMed
Google Scholar
Goldstein ND, Sarwate AD. Privacy, security, and the public health researcher in the era of electronic health record research. Online J Public Health Inform. 2016;8(3):e207.
Article
PubMed
PubMed Central
Google Scholar
U.S. Department of Health and Human Services (HHS). 45 CFR 46. http://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html.