Cited by 6Medical Coding and Health InformationElectronic Health Records SystemsData Quality and Management

The DOI matches its Crossref record.

Measuring Disparities in Information Capture Timeliness Across Healthcare Settings: Effects on Data Quality

Lorence, Daniel·2003·Journal of Medical Systems

Abstract

The emergence of evidence-based medicine in the United States has created an industry-wide environment where the quality of data maintained by healthcare organizations is becoming a critical factor in the delivery of medical care. Such a transition necessitates a corresponding need for consistent data collection and maintenance methods. In this study results of a national survey of health information managers were used to assess prevalence of a standard data quality practice, the adoption of policies related to timeliness of data capture. Findings from this survey show that, on a national level, only a slight majority of respondents indicated adoption of timeliness policies. About 61% of respondents indicate they have policies and procedures addressing data timeliness, although persistent patterns of nonadoption were found. We examine how the timeliness of data collection might serve as part of an overall data collection strategy that managers can employ to improve the quality of their information.

Overview

Integrity signals

Registry

Crossref · 10.1023/a:1025655721518

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 14

  1. Lorence, D., Benchmarking quality under USA health care reform: The next generation. Qual. Prog. 27(4):103–107, 1994.
  2. Mendelson, H., and Kraemer, K. L., Clockspeed and information response: Evidence from the information technology industry. Inf. Syst. Res. Dec.:415–433, 1998. · DOI
  3. Ballou, D., and Pazer, H., Designing information systems to optimize the accuracy-timeliness tradeoff. Inf. Syst. Res. 6:51–72, 1995. · DOI
  4. Fox, C., Levitin, A., and Redman, T. C., The notion of data and its quality dimensions. Inf. Process. Manage. 30:9–19, 1993. · DOI
  5. Frates, J., Ginty, M. J., and Baker, L., Optimizing claims payment for successful risk management. Health Finance Manage. 56(5):56–60, 2002.
  6. Ballou, D., Wang, R., Pazer, H., and Tayi, G., Modeling information manufacturing systems to determine information product quality. Manage. Sci. 44:462–484, 1998. · DOI
  7. Coats, B., 1–2–3 Guide to Medical Billing, National Association of Claims Processing Professionals, Des Plaines, IL, 2000.
  8. Sakamoto, N., Object-oriented development of a concept learning system for time-centered clinical data. J. Med. Syst. 20(4):183–196, 1996. · DOI
  9. Sicotte, C., Denis, J. L., Lehoux, P., and Champagne, F., The computer-based patient record challenges towards timeless and spaceless medical practice. J. Med. Syst. 22(4):237–256, 1998. · DOI
  10. Vassilacopoulos, G., and Paraskevopoulou, E. A process model basis for evolving hospital information systems, J. Med. Syst. 21(3):141–153, 1997. · DOI
  11. Lorence, D. P., and Spink, A., Regional variation in medical systems data: Influences on upcoding. J. Med. Syst. 26(5):369–381, 2002. · DOI
  12. Lorence, D., and Spink, A., Regional Variation in Medical Data Quality Management. Proc. 2002 Inf. Res. Manage. Assoc. Int. Conf. Seattle, WA, 2002.
  13. Lorence, D., National implementation patterns of computer-based patient records, Proc. Natl. Conf. on Health Stat. NCHVS, Washington, DC, Aug 1999.
  14. Sparrow, M., Assessment of existing fraud control systems. In License to Steal, Westview Press, Boulder, CO, 2000.

Cited by

Sorted by citations

Metadata

Authors
Lorence, D.
Affiliations
Pennsylvania State University (US)
Corresponding author
Lorence, Daniel
Venue
Journal of Medical Systems
Published
2003-10
Volume
27
Issue
5
Pages
425-433
ISSN
  • 0148-5598 (ISSN-L)
  • 1573-689X
OpenAlex
W1544374119
Cited by
6
References
0
Citations by year
2025: 1 · 2020: 1 · 2016: 1 · 2014: 2
FWCI
0.00
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Medical Systems (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Physical Sciences › Computer Science › Information Systems › Data Quality and Management
Keywords
data timeliness, data quality, health information management, data capture, health data collection, health information managers, evidence-based medicine
MeSH terms
Data Collection, Forms and Records Control, Health Facilities, Humans, Information Systems, Medical Record Administrators, Time, United States, Organizational Policy, Medical Records Systems, Computerized, Information Management, Health Care Surveys, Health Care Sector

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