CoChat
Sign Up
Cited by 4Electronic Health Records SystemsMedical Coding and Health InformationHealthcare Systems and Technology

Patient-Centered Data Home: A Path Towards National Interoperability

Open access
Williams, Karmen S., Grannis, Shaun J.·2022·Frontiers in Digital Health
DOI 10.3389/fdgth.2022.887015PMC PMC9328272
Open this paper in CoChat — free accountOpen PDF

Abstract

Objective: National interoperability is an agenda that has gained momentum in health care. Although several attempts to reach national interoperability, an alerting system through interconnected network of Health Information Exchange (HIE) organizations, Patient-Centered Data Home (PCDH), has seen preliminary success. The aim was to characterize the PCDH initiative through the Indiana Health Information Exchange's participation in the Heartland Region Pilot, which includes HIEs in Indiana, Ohio, Michigan, Kentucky, and Tennessee. Materials and Methods: Admission, Discharge, and Transfer (ADT) transactions were collected between December 2016 and December 2017 among the seven HIEs in the Heartland Region. ADTs were parsed and summarized. Overlap analyses and patient matching software were used to characterize the PCDH patients. R software and Microsoft Excel were used to populate descriptive statistics and visualization. Results: Approximately 1.5 million ADT transactions were captured. Majority of patients were female, ages 56–75 years, and were outpatient visits. Top noted reasons for visit were labs, screening, and abdominal pain. Based on the overlap analysis, Eastern Tennessee HIE was the only HIE with no duplicate service areas. An estimated 80 percent of the records were able to be matched with other records. Discussion: The high volume of exchange in the Heartland Region Pilot established that PCDH is practical and feasible to exchange data. PCDH has the posture to build better comprehensive medical histories and continuity of care in real time. Conclusion: The value of the data gained extends beyond clinical practitioners to public health workforce for improved interventions, increased surveillance, and greater awareness of gaps in health for needs assessments. This existing interconnection of HIEs has an opportunity to be a sustainable path toward national interoperability.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fdgth.2022.887015

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 23

  1. [2]Williams KS Shah G Leider JP Gupta A. Overcoming barriers to experience benefits: a qualitative analysis of electronic health records and health information exchange implementation in local health departments. eGEMS. (2017) 5: 18. 10.5334/egems.216 29881738 PMC5983057 · DOI
  2. [3]Cross DA Stevens MA Spivack SB Murray GF Rodriguez HP Lewis VA. Survey of information exchange and advanced use of other health information technology in primary care settings: capabilities in and outside of the safety net. Med Care. (2022) 60: 140– 8. 10.1097/MLR.0000000000001673 35030563 PMC8966676 · DOI
  3. [4]Epstein R Street R. The values and value of patient-centered care. Ann Fam Med. (2011) 9: 100– 3. 10.1370/afm.1239 21403134 PMC3056855 · DOI · Paper page
  4. [5]Horahan K Morchel H Raheem M Stevens L. Electronic health records access during a disaster. Online J Public Health Inform. (2014) 5: e232. 10.5210/ojphi.v5i3.4826 24683443 PMC3959913 · DOI
  5. [6]Chen J Singer E. Primary care follow-up after emergency department visits for routine complaints: What primary care physicians prefer and what emergency department physicians currently recommend. Pediatr Emerg Care. (2016) 32: 371– 6. 10.1097/PEC.0000000000000314 25695845 · DOI
  6. [9]Jackson C Shahsahebi M Wedlake T DuBard CA. Timeliness of outpatient follow-up: an evidence-based approach for planning after hospital discharge. Ann Fam Med. (2015) 13: 115– 22. 10.1370/afm.1753 25755032 PMC4369604 · DOI
  7. [10]Sinha S Seirup J Carmel A. Early primary care follow-up after ED and hospital discharge—does it affect readmissions? Hosp Prac. (2017) 45: 51– 7. 10.1080/21548331.2017.1283935 28095063 · DOI
  8. [11]DeLia D Tong J Gaboda D Casalino LP. Post-discharge follow-up visits and hospital utilization by Medicare patients, 2007–2010. Medicare Medicaid Res Rev. (2014) 4: mmrr.004.02.a01. 10.5600/mmrr.004.02.a01 24949226 PMC4062381 · DOI
  9. [12]Fecher K McCarthy L Porreca D Yaraghi N. Assessing the Benefits of Integrating Health Information Exchange Services into the Medical Practices' Workflow. Inform Sys Frontiers. (2020) 23: 599– 605. 10.1007/s10796-019-09979-x · DOI
  10. [13]Han X Lowry TY Loo GT Rabin EJ Grinspan ZM Kern LM Kuperman GJ Shapiro JS. Expanding health information exchange improves identification of frequent emergency department users. Ann Emerg Med. (2019) 73: 172– 9. 10.1016/j.annemergmed.2018.07.024 30236418 PMC12945391 · DOI
  11. [14]Miller AR Tucker C. Health information exchange, system size and information silos. J Health Econ. (2014) 33: 28– 42. 10.1016/j.jhealeco.2013.10.004 24246484 · DOI
  12. [15]Chandrasekaran R Sankaranarayanan B Pendergrass J. Unfulfilled promises of health information exchange: what inhibits ambulatory clinics from electronically sharing health information? Intl J Med Informatics. (2021) 149: 104418. 10.1016/j.ijmedinf.2021.104418 33640839 · DOI
  13. [16]Nakayama M Inoue R Miyata S Shimizu H. Health information exchange between specialists and general practitioners benefits rural patients. Appl Clin Inform. (2021) 12: 564– 72. 10.1055/s-0041-1731287 34107543 PMC8189760 · DOI
  14. [17]Shen N Bernier T Sequeira L Strauss J Pannor Silver M Carter-Langford A. Understanding the patient privacy perspective on health information exchange: a systematic review. Intl J Med Inform. (2019) (125): 1– 12. 10.1016/j.ijmedinf.2019.01.014 30914173 · DOI
  15. [19]Payne TH Lovis C Gutteridge C. Status of health information exchange: a comparison of six countries. J Glob Health. (2019) 9: 0204279. 10.7189/jogh.09.020427 31673351 PMC6815656 · DOI
  16. [20]Misky GJ Wald HL Coleman EA. Post-hospitalization transitions: examining the effects of timing on primary care provider follow-up. J Hosp Med. (2010) 5: 392– 7. 10.1002/jhm.666 20578046 · DOI
  17. [21]Menachemi N Rahurkar S Harle C Vest J. The benefits of health information exchange: an updated systematic review. J Am Med Informatics Assoc. (2018) 25: 1259– 65. 10.1093/jamia/ocy035 29718258 PMC7646861 · DOI · Paper page
  18. [23]Grannis SJ Overhage JM Hui S McDonald C. Analysis of a probabilistic record linkage technique without human review. AMIA Ann Symp Proc. (2003) 2003: 259– 63. 14728174 PMC1479910
  19. [24]Finnell JT Overhage JM Grannis S. All health care is not local: an evaluation of the distribution of emergency department care delivered in Indiana. AMIA Annu Symp Proc. 211: 409– 16. 22195094 PMC3243262
  20. [25]Grannis SJ Xu H Vest JR Kasthurirathne S Bo N Moscovitch B. Evaluating the effect of data standardization and validation on patient matching accuracy. J Am Med Informatics Assoc. (2019) 26: 447– 56. 10.1093/jamia/ocy191 30848796 PMC7787357 · DOI
  21. [29]Adler-Midstein J Garg A Zhao W Patel V. A survey of health information exchange organizations in advance of a nationwide connectivity framework. Health Aff. (2021) 40: 736– 44. 10.1377/hlthaff.2020.01497 33939510 · DOI
  22. [31]American Medical Association (2018). CPT 2019 [Professional Edition]. Chicago, IL. (accessed May 18, 2019).
  23. [33]VanHouten JP Brandt CA. Universal Patient Identification: What it is Why the US Needs it. (2021). Available online at: https://www.healthaffairs.org/do/10.1377/forefront.20210701.888615/ (accessed June 5, 2022). · DOI

Cited by

Sorted by citations

Authors
Williams, K. S., & Grannis, S. J.
Affiliations
  • City University of New York (US)
  • Regenstrief Institute (US)
Corresponding author
Williams, Karmen S.
ORCID
Venue
Frontiers in Digital Health
Publisher
Frontiers Media SA
Published
2022-07-13
Volume
4
Pages
887015-887015
ISSN
2673-253X (ISSN-L)
DOI
10.3389/fdgth.2022.887015
PMC
PMC9328272
OpenAlex
W4285087982
Cited by
4
References
0
Citations by year
2025: 4
FWCI
0.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health
  • U.S. National Library of Medicine
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Healthcare Systems and Technology
Keywords
health information exchange, continuity of care, disease surveillance, healthcare workers

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review