Using EHR data to predict hospital-acquired pressure ulcers: A prospective study of a Bayesian Network model

International Journal of Medical Informatics

Volumn 82, Issue 11, 2013, Pages 1059-1067

  Cho, Insook ^a,c,d   Park, Ihnsook ^b   Kim, Eunman ^e   Lee, Eunjoon ^b   Bates, David W ^c,d  

^a Inha University   (South Korea)

^b Seoul National University Hospital   (South Korea)

^c BRIGHAM AND WOMEN S HOSPITAL   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

^e SunMoon University   (South Korea)

Author keywords

Clinical decision support; Electronic health records; Hospital acquired pressure ulcers; Prevalence; Prevention

Indexed keywords

CLINICAL DECISION SUPPORT; ELECTRONIC HEALTH RECORD; PRESSURE ULCERS; PREVALENCE; PREVENTION;

BAYESIAN NETWORKS; DECISION SUPPORT SYSTEMS; DISEASES; MANAGEMENT; MANAGERS; PATIENT TREATMENT;

INTENSIVE CARE UNITS;

ADULT; ARTICLE; BAYES THEOREM; DECUBITUS; DISEASE SEVERITY; FEMALE; HOSPITAL ACQUIRED PRESSURE ULCER; HUMAN; INTENSIVE CARE UNIT; LENGTH OF STAY; MAJOR CLINICAL STUDY; MALE; PATIENT ATTITUDE; PREDICTION; PREVALENCE; PRIORITY JOURNAL; PROSPECTIVE STUDY;

CLINICAL DECISION SUPPORT; ELECTRONIC HEALTH RECORDS; HOSPITAL-ACQUIRED PRESSURE ULCERS; PREVALENCE; PREVENTION;

ADULT; AGED; BAYES THEOREM; DECISION SUPPORT SYSTEMS, CLINICAL; ELECTRONIC HEALTH RECORDS; FEMALE; HOSPITALIZATION; HOSPITALS, TEACHING; HUMANS; MALE; MIDDLE AGED; MODELS, THEORETICAL; PRESSURE ULCER; PREVALENCE; PROSPECTIVE STUDIES; REPUBLIC OF KOREA; RISK FACTORS;

EID: 84886788907     PISSN: 13865056     EISSN: 18728243     Source Type: Journal    
DOI: 10.1016/j.ijmedinf.2013.06.012     Document Type: Article

References (40)

1. Ayello E., Sibbald R. Preventing Pressure Ulcers and Skin Tears 2008, Spinger Publishing Company, New York (NY).
2. Agency for Healthcare Research and Quality, Pressure ulcers are increasing among hospital patients, Research Activities, January 2009, No. 341. Publication # 09-RA004.
3. Russo C.A., Steiner C., Spector W. Hospitalizations related to pressure ulcers among adults 18 years and older, 2006. HCUP Statistical Brief #64. Healthcare Cost and Utilization Project AHRQ 2008.
4. Bennett G., Dealey C., Posnett J. The cost of pressure ulcers in the UK. Age Ageing 2004, 33(May (3)):230-235.
5. Cuddigan J., Berlowitz D., Ayello E. Pressure ulcers in America: prevalence, incidence, and implications for the future: an executive summary of the National Pressure Ulcer Advisory Panel monograph. Copyright 2001 NPUAP. Reprinted with permission. Adv. Skin Wound Care 2001, 14(4 (Pt. 1)):208-215.
6. Whittington K., Briones R. National prevalence and incidence study: 6-year sequential acute care data. Adv. Skin Wound Care 2004, 17(9):490-494.
7. van den Bos J., Rustagi K., Gray T., Halford M., Ziemkiewicz E., Shreve J. The $17.1 billion problem: the annual cost of measurable medical errors. Health Aff. (Millwood) 2011, 30(4):596-603.
8. Brown S.J. The Braden Scale: a review of the research evidence. Orthop. Nurs. 2004, 23(1):30-38.
9. Bergstrom N., Braden B.J. Brief report. Predictive validity of the Braden Scale among black and white subjects. Nurs. Res. 2002, 51(6):398-403.
10. Defloor T., Grypdonck M.F. Pressure ulcers: validation of two risk assessment scales. J. Clin. Nurs. 2005, 14(3):373-382.
11. Kim T.Y., Lang N. Predictive modeling for the prevention of hospital-acquired pressure ulcers. AMIA Annu. Symp. Proc. 2006, 434-438.
12. Brown D.S., Donaldson N., Aydin C.E., Carlson N. Hospital nursing benchmarks: the California Nursing Outcomes Coalition Project. J. Healthc. Qual.: Promot. Excel. Healthc. 2001, 23(4):22-27.
13. Wolverton C.L., Hobbs L.A., Beeson T., et al. Nosocomial pressure ulcer rates in critical care: performance improvement project. J. Nurs. Care Qual. 2005, 20(1):56-62.
14. Cho I., Noh M. Braden Scale: evaluation of clinical usefulness in an intensive care unit. J. Adv. Nurs. 2010, 66(2):293-302.
15. Cho I., Park H.A., Chung E. Exploring practice variation in preventive pressure-ulcer care using data from a clinical data repository. Int. J. Med. Inf. 2011, 80(January (1)):47-55.
16. Reddy M., Gill S.S., Rochon P.A. Preventing pressure ulcers: a systematic review. JAMA 2006, 296(August 23 (8)):974-984.
17. McInnes E., Jammali-Blasi A., Bell-Syer S.E.M., Dumville J.C., Cullum N. Support surfaces for pressure ulcer prevention. Cochrane Database Syst. Rev. 2011, 2011(4).
18. Iglesias C. Pressure relieving support surfaces (PRESSURE) trial: cost effectiveness analysis (vol. 332, p. 1416, 2006). Br. Med. J. 2006, 333(August 12 (7563)):339.
19. Defloor T., de Bacquer D., Grypdonck M.H.F. The effect of various combinations of turning and pressure reducing devices on the incidence of pressure ulcers. Int. J. Nurs. Stud. 2005, 42(January (1)):37-46.
20. de Laat E.H., Schoonhoven L., Pickkers P., Verbeek A.L., van Achterberg T. Implementation of a new policy results in a decrease of pressure ulcer frequency. Int. J. Qual. Health Care: J. Int. Soc. Qual. Health Care/Isqua 2006, 18(2):107-112.
21. Baldelli P., Paciella M. Creation and implementation of a pressure ulcer prevention bundle improves patient outcomes. Am. J. Med. Qual. 2008, 23(March and April (2)):136-142.
22. European Pressure Ulcer Advisory Panel. Prevention and Treatment of Pressure Ulcers: Clinical Practice Guideline 2009, National Pressure Ulcer Advisory Panel, National Pressure Ulcer Advisory Panel, Washington, DC.
23. Pressure ulcers in adults: prediction and prevention. Clin. Prac. Guidel. 1992, Agency for Health Care Policy and Research (AHCPR), (Number 3, AHCPR Pub. No. 92-0047 (last revision January 10, 2007): available at: http://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=hstat2.chapter.4409 (last accessed July 4401, 2009)).
24. Cho I., Park S., Jung E. Availability of nursing data in an electronic medical record system for assessing the risk of pressure ulcers. AMIA Annual Symposium proceedings/AMIA Symposium. AMIA Symposium. 2008 November 2008, 905.
25. Cho I., Im P.S., Yoon H.Y., Sook L.H. Availability of nursing data in an electronic nursing record system for a development of a risk assessment tool for pressure ulcers. J. Korean Soc. Med. Inform. 2008, 14:161-168.
26. Cho I. Assessing the quality of structured data entry for the secondary use of electronic medical records. J. Korean Soc. Med. Inform. 2009, 15:423-431.
27. Park I.S., Cho I., Kim E.M. Predictive Bayesian Network model for hospital-acquired pressure ulcers for ICU patients. Stud. Health Technol. Inform. 2012, 147:630.
28. Cho I.S., Chung E. Predictive Bayesian network model using electronic patient records for prevention of hospital-acquired pressure ulcers. J. Korean Acad. Nurs. 2011, 41(June (3)):423-431.
29. Black J., Baharestani M., Cuddigan J., et al. National Pressure Ulcer Advisory Panel's updated pressure ulcer staging system. Dermatology Nursing/Dermatology Nurses' Association, vol. 19, August (4) 2007, 343-350.
30. Venkatesh V., Morris M.G., Davis G.B., Davis F.D. User acceptance of information technology: toward a unified view. Mis. Q. 2003, 27(September (3)):425-478.
31. Willson D., Ashton C., Wingate N., et al. Computerized support of pressure ulcer prevention and treatment protocols. Proceedings/the Annual Symposium on Computer Application [sic] in Medical Care. Symposium on Computer Applications in Medical Care 1995, 646-650.
32. Dowding D.W., Turley M., Garrido T. The impact of an electronic health record on nurse sensitive patient outcomes: an interrupted time series analysis. J. Am. Med. Inform. Assoc. 2012, 19(July (4)):615-620.
33. Zielstorff R.D., Barnett G.O., Fitzmaurice J.B., et al. A decision support system for prevention and treatment of pressure ulcers based on AHCPR guidelines. Proceedings: a Conference of the American Medical Informatics Association, AMIA Fall Symposium 1996, 562-566.
34. Zielstorff R.D., Estey G., Vickery A., Hamilton G., Fitzmaurice J.B., Barnett G.O. Evaluation of a decision support system for pressure ulcer prevention and management: preliminary findings. J. Am. Med. Inform. Assoc. 1997, 1997:248-252.
35. Himes B.E., Dal Y., Kohane I.S., Weiss S.T., Ramoni M.F. Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records. J. Am. Med. Inform. Assoc. 2009, 16(May and June (3)):371-379.
36. Crump C., Silvers C.T., Wilson B., Schlachta-Fairchild L., Lingley-Papadopoulos C.A., Ashley J.S. Using principal component analysis to aid Bayesian Network development for prediction of critical care patient outcomes. J. Trauma - Inj. Infect. Crit. Care 2011, 71(December (6)):1841-1849.
37. Sebastiani P., Mandl K.D., Szolovits P., Kohane I.S., Ramoni M.F. A Bayesian dynamic model for influenza surveillance. Stat. Med. 2006, 25(11):1803-1816.
38. Bellazzi R., Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Inform. 2008, 77(2):81-97.
39. Goldgar D.E., Easton D.F., Byrnes G.B., Spurdle A.B., Iversen E.S., Greenblatt M.S. Genetic evidence and integration of various data sources for classifying uncertain variants into a single model. Hum. Mutat. 2008, 29(11):1265-1272.
40. Randell R., Dowding D. Organisational influences on nurses' use of clinical decision support systems. Int. J. Med. Inf. 2010, 79(June (6)):412-421.