April 28, 2025

Harmony Thrive

Superior Health, Meaningful Life

Data linkage multiplies research insights across diverse healthcare sectors

Data linkage multiplies research insights across diverse healthcare sectors
  • Weber, G. M., Mandl, K. D. & Kohane, I. S. Finding the missing link for big biomedical data. JAMA 311, 2479–2480 (2014).

    CAS 
    PubMed 

    Google Scholar 

  • Stange, K. C. The problem of fragmentation and the need for integrative solutions. Ann. Fam. Med. 7, 100–103 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Cebul, R. D., Rebitzer, J. B., Taylor, L. J. & Votruba, M. E. Organizational fragmentation and care quality in the U.S healthcare system. J. Econ. Perspect. 22, 93–113 (2008).

    Article 
    PubMed 

    Google Scholar 

  • Song, J. et al. Utilization of electronic health record data to evaluate the association of urban environment on systemic lupus erythematosus symptoms. Rheumatology (Oxford). (2022).

  • Walunas, T. L. et al. Disease outcomes and care fragmentation among patients with systemic lupus erythematosus. Arthritis Care Res. 69, 1369–1376 (2017).

    Article 

    Google Scholar 

  • Adler-Milstein, J., Bates, D. W. & Jha, A. K. A survey of health information exchange organizations in the United States: implications for meaningful use. Ann. Intern Med. 154, 666–671 (2011).

    Article 
    PubMed 

    Google Scholar 

  • Krieger, N. The US Census and the People’s Health: public health engagement from enslavement and “indians not taxed” to census tracts and health equity (1790-2018). Am. J. Public Health 109, 1092–1100 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lorkowski, J. & Pokorski, M. Medical records: a historical narrative. Biomedicines 10, 2594 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Camp, C. L. et al. Patient records at Mayo Clinic: lessons learned from the first 100 patients in Dr Henry S. Plummer’s dossier model. Mayo Clin. Proc. 83, 1396–1399 (2008).

    Article 
    PubMed 

    Google Scholar 

  • Castellucci, M. Road to the Mayo Clinic: Plummer’s novel ideas transformed healthcare. Mod. Healthcare 46, H10–H12 (2016).

  • Dunn, H. L. Record linkage. Am. J. Public Health Nations Health 36, 1412–1416 (1946).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fellegi, I. P. & Sunter, A. B. A theory for record linkage. J. Am. Stat. Assoc. 64, 1183–1210 (1969).

  • Ruggles, S., Flood, S., Goeken, R., Schouweiler, M. & Sobek, M. IPUMS USA: Version 15.0 [dataset]. (IPUMS, Minneapolis, MN, 2023).

  • Mennemeyer, S. T., Menachemi, N., Rahurkar, S. & Ford, E. W. Impact of the HITECH Act on physicians’ adoption of electronic health records. J. Am. Med. Inform. Assoc. 23, 375–379 (2016).

    Article 
    PubMed 

    Google Scholar 

  • Cohen, M. F. Impact of the HITECH financial incentives on EHR adoption in small, physician-owned practices. Int. J. Med. Inform. 94, 143–154 (2016).

    Article 
    PubMed 

    Google Scholar 

  • Joseph, S., Sow, M., Furukawa, M. F., Posnack, S. & Chaffee, M. A. HITECH spurs EHR vendor competition and innovation, resulting in increased adoption. Am. J. Manag. Care 20, 734–740 (2014).

    PubMed 

    Google Scholar 

  • Szarfman, A. et al. Recommendations for achieving interoperable and shareable medical data in the USA. Commun. Med. 2, 86 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wu, S. et al. Deep learning in clinical natural language processing: a methodical review. J. Am. Med. Inf. Assoc. 27, 457–470 (2020).

    Article 

    Google Scholar 

  • Ong, T. C., Duca, L. M., Kahn, M. G. & Crume, T. L. A hybrid approach to record linkage using a combination of deterministic and probabilistic methodology. J. Am. Med. Inf. Assoc. 27, 505–513 (2020).

    Article 

    Google Scholar 

  • Joffe, E. et al. A benchmark comparison of deterministic and probabilistic methods for defining manual review datasets in duplicate records reconciliation. J. Am. Med. Inf. Assoc. 21, 97–104 (2014).

    Article 

    Google Scholar 

  • Weber, S. C., Lowe, H., Das, A. & Ferris, T. A simple heuristic for blindfolded record linkage. J. Am. Med. Inf. Assoc. 19, e157–e161 (2012).

    Article 

    Google Scholar 

  • Grannis, S. J., Williams, J. L., Kasthuri, S., Murray, M. & Xu, H. Evaluation of real-world referential and probabilistic patient matching to advance patient identification strategy. J. Am. Med. Inf. Assoc. 29, 1409–1415 (2022).

    Article 

    Google Scholar 

  • Deng, Y. et al. Evolving availability and standardization of patient attributes for matching. Health Aff. Scholar 1, qxad047 (2023).

    Article 

    Google Scholar 

  • culbertson, A. et al. The building blocks of inter-operability: a multisite analysis of patient demographic attributes available for matching. Appl. Clin. Inform. 08, 322–336 (2017).

    Article 

    Google Scholar 

  • Krzyzanowski, B. & Manson, S. M. Twenty years of the health insurance portability and accountability act safe harbor provision: unsolved challenges and ways forward. JMIR Med. Inf. 10, e37756 (2022).

    Article 

    Google Scholar 

  • Kum, H. C., Krishnamurthy, A., Machanavajjhala, A., Reiter, M. K. & Ahalt, S. Privacy preserving interactive record linkage (PPIRL). J. Am. Med. Inform. Assoc. 21, 212–220 (2014).

    Article 
    PubMed 

    Google Scholar 

  • Mirel, L. B., Resnick, D. M., Aram, J. & Cox, C. S. A methodological assessment of privacy preserving record linkage using survey and administrative data. Stat. J. IAOS 38, 413–421 (2022).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Nguyen, L. et al. Privacy-preserving record linkage of deidentified records within a public health surveillance system: evaluation study. J. Med. Internet Res. 22, e16757 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Irvine, K. et al. Real world performance of privacy preserving record linkage. Int. J. Population Data Sci. 3. (2018).

  • Kho, A. N. et al. Design and implementation of a privacy preserving electronic health record linkage tool in Chicago. J. Am. Med Inf. Assoc. 22, 1072–1080 (2015).

    Article 

    Google Scholar 

  • Kho, A. N. et al. in Machine Learning and Knowledge Discovery in Databases. (eds Peggy Cellier & Kurt Driessens) 79-87 (Springer International Publishing, 2022).

  • Yang, Y. et al. Ancillary Data Record Linkage to characterize the completeness of data for the All of Us Research Program. Int. J. Popul. Data Sci. 7. (2022).

  • Marsolo, K. et al. Assessing the impact of privacy-preserving record linkage on record overlap and patient demographic and clinical characteristics in PCORnet(R), the National Patient-Centered Clinical Research Network. J. Am. Med Inf. Assoc. 30, 447–455 (2023).

    Article 

    Google Scholar 

  • Kiernan, D. et al. Establishing a framework for privacy-preserving record linkage among electronic health record and administrative claims databases within PCORnet((R)), the National Patient-Centered Clinical Research Network. BMC Res Notes 15, 337 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sidky, H. et al. Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C). BMC Med. Res. Methodol. 23, 46 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Khurshid, A. et al. Social and health information platform: piloting a standards-based, digital platform linking social determinants of health data into clinical workflows for community-wide use. Appl. Clin. Inform. 14, 883–892 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Graham, R. J. et al. Real-world analysis of healthcare resource utilization by patients with X-linked myotubular myopathy (XLMTM) in the United States. Orphanet J. Rare Dis. 18, 138 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Benitez, K., Loukides, G. & Malin, B. Beyond safe harbor: automatic discovery of health information de-identification policy alternatives. IHI 2010, 163–172 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • El Emam, K. et al. A globally optimal k-anonymity method for the de-identification of health data. J. Am. Med. Inf. Assoc. 16, 670–682 (2009).

    Article 

    Google Scholar 

  • Blackport, J., Moffatt, C., Symmers, P., Bayless, P. & Gray, J. Methods and systems for monitoring a risk of re‐identification in a de‐identified database. U.S. Patent No. 11,741,262 B2 (2023). Filed July 19, 2021; issued August 29, 2023.

  • Baker, D. B., Kaye, J. & Terry, S. F. Governance through privacy, fairness, and respect for individuals. EGEMS 4, 1207 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bjornevik, K. et al. Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis. Science 375, 296–301 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Lanz, T. V. et al. Clonally expanded B cells in multiple sclerosis bind EBV EBNA1 and GlialCAM. Nature 603, 321–327 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Opie-Martin, S. et al. The SOD1-mediated ALS phenotype shows a decoupling between age of symptom onset and disease duration. Nat. Commun. 13, 6901 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Benatar, M. et al. Design of a randomized, Placebo-Controlled, Phase 3 trial of tofersen initiated in clinically presymptomatic SOD1 variant carriers: the ATLAS study. Neurotherapeutics 19, 1248–1258 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Afshar, M. et al. Creation of a data commons for substance misuse related health research through privacy-preserving patient record linkage between hospitals and state agencies. JAMIA Open 6, ooad092 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chin, R. F. M., Pickrell, W. O., Guelfucci, F., Martin, M. & Holland, R. Prevalence, healthcare resource utilization and mortality of Lennox-Gastaut syndrome: retrospective linkage cohort study. Seizure 91, 159–166 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Pathak, A. et al. Privacy preserving record linkage for public health action: opportunities and challenges. J. Am. Med. Inform. Assoc. 31, 2605–2612 (2024).

  • Haendel, M. A. et al. The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment. J. Am. Med. Inf. Assoc. 28, 427–443 (2021).

    Article 

    Google Scholar 

  • Ando, W. et al. Impact of overlapping risks of type 2 diabetes and obesity on coronavirus disease severity in the United States. Sci. Rep. 11, 17968 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bronstein, J. M. et al. Issues and biases in matching medicaid pregnancy episodes to vital records data: the Arkansas experience. Matern Child Health J. 13, 250–259 (2009).

    Article 
    PubMed 

    Google Scholar 

  • Cole, J. A. et al. Bupropion in pregnancy and the prevalence of congenital malformations. Pharmacoepidemiol. Drug Saf. 16, 474–484 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Cole, J. A., Ephross, S. A., Cosmatos, I. S. & Walker, A. M. Paroxetine in the first trimester and the prevalence of congenital malformations. Pharmacoepidemiol. Drug Saf. 16, 1075–1085 (2007).

    Article 
    PubMed 

    Google Scholar 

  • Grzeskowiak, L. E., Gilbert, A. L. & Morrison, J. L. Methodological challenges in using routinely collected health data to investigate long-term effects of medication use during pregnancy. Ther. Adv. Drug Saf. 4, 27–37 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Balan, N., Petrie, B. A. & Chen, K. T. Racial disparities in colorectal cancer care for black patients: barriers and solutions. Am. Surg. 88, 2823–2830 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Hwang, C. S. Black, incarcerated, and dying: reflections on racism and inequities in health care. Ann. Intern Med. 175, 1047–1048 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Lillard, J. W. Jr., Moses, K. A., Mahal, B. A. & George, D. J. Racial disparities in Black men with prostate. Cancer A Lit. Rev. Cancer. 128, 3787–3795 (2022).

    Google Scholar 

  • Tobin, M. J. Fiftieth anniversary of uncovering the tuskegee syphilis study: the story and timeless lessons. Am. J. Respir. Crit. Care Med. 205, 1145–1158 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jarrell, R. H. Native American women and forced sterilization, 1973-1976. Caduceus 8, 45–58 (1992).

    CAS 
    PubMed 

    Google Scholar 

  • Lawrence, J. The Indian Health Service and the sterilization of Native American women. Am. Indian Q 24, 400–419 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Swartz, T. H. & Titanji, B. Deconstruct racism in medicine – from training to clinical trials. Nature 583, 202 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Rai, T., Hinton, L., McManus, R. J. & Pope, C. What would it take to meaningfully attend to ethnicity and race in health research? Learning from a trial intervention development study. Socio. Health Illn. 44, 57–72 (2022).

    Article 

    Google Scholar 

  • Shah, S. J. & Essien, U. R. Equitable representation in clinical trials: looking beyond table 1. Circ. Cardiovasc. Qual. Outcomes 15, e008726 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Azizi, Z. et al. Can synthetic data be a proxy for real clinical trial data? A validation study. BMJ Open 11, e043497 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dasaradharami Reddy, K. & Gadekallu, T. R. A comprehensive survey on federated learning techniques for healthcare informatics. Comput. Intell. Neurosci. 2023, 8393990 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • van Egmond, M. B. et al. Privacy-preserving dataset combination and Lasso regression for healthcare predictions. BMC Med. Inf. Decis. Mak. 21, 266 (2021).

    Article 

    Google Scholar 

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