Healthcare Provider Perceptions of Clinical Prediction Rules

Overview

Journal BMJ Open

Specialty General Medicine

Date 2015 Sep 5

PMID 26338684

Citations 3

Authors

Safiya Richardson

Sundas Khan

Lauren McCullagh

Myriam Kline

Devin Mann

Thomas McGinn

Affiliations

Soon will be listed here.

Abstract

Objectives: To examine internal medicine and emergency medicine healthcare provider perceptions of usefulness of specific clinical prediction rules.

Setting: The study took place in two academic medical centres. A web-based survey was distributed and completed by participants between 1 January and 31 May 2013.

Participants: Medical doctors, doctors of osteopathy or nurse practitioners employed in the internal medicine or emergency medicine departments at either institution.

Primary And Secondary Outcome Measures: The primary outcome was to identify the clinical prediction rules perceived as most useful by healthcare providers specialising in internal medicine and emergency medicine. Secondary outcomes included comparing usefulness scores of specific clinical prediction rules based on provider specialty, and evaluating associations between usefulness scores and perceived characteristics of these clinical prediction rules.

Results: Of the 401 healthcare providers asked to participate, a total of 263 (66%), completed the survey. The CHADS2 score was chosen by most internal medicine providers (72%), and Pulmonary Embolism Rule-Out Criteria (PERC) score by most emergency medicine providers (45%), as one of the top three most useful from a list of 24 clinical prediction rules. Emergency medicine providers rated their top three significantly more positively, compared with internal medicine providers, as having a better fit into their workflow (p=0.004), helping more with decision-making (p=0.037), better fitting into their thought process when diagnosing patients (p=0.001) and overall, on a 10-point scale, more useful (p=0.009). For all providers, the perceived qualities of useful at point of care, helps with decision making, saves time diagnosing, fits into thought process, and should be the standard of clinical care correlated highly (≥0.65) with overall 10-point usefulness scores.

Conclusions: Healthcare providers describe clear preferences for certain clinical prediction rules, based on medical specialty.

Citing Articles

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References

Seaberg D, Jackson R . Clinical decision rule for knee radiographs. Am J Emerg Med. 1994; 12(5):541-3. DOI: 10.1016/0735-6757(94)90274-7. View

Wisnivesky J, Kaplan J, Henschke C, McGinn T, Crystal R . Evaluation of clinical parameters to predict Mycobacterium tuberculosis in inpatients. Arch Intern Med. 2000; 160(16):2471-6. DOI: 10.1001/archinte.160.16.2471. View

Laupacis A, Sekar N, Stiell I . Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA. 1997; 277(6):488-94. View

Kamath P, Wiesner R, Malinchoc M, Kremers W, Therneau T, Kosberg C . A model to predict survival in patients with end-stage liver disease. Hepatology. 2001; 33(2):464-70. DOI: 10.1053/jhep.2001.22172. View

Gage B, Waterman A, Shannon W, Boechler M, Rich M, Radford M . Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA. 2001; 285(22):2864-70. DOI: 10.1001/jama.285.22.2864. View

Stiell I, Wells G, Vandemheen K, Clement C, Lesiuk H, De Maio V . The Canadian C-spine rule for radiography in alert and stable trauma patients. JAMA. 2001; 286(15):1841-8. DOI: 10.1001/jama.286.15.1841. View

Lim W, van der Eerden M, Laing R, Boersma W, Karalus N, Town G . Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003; 58(5):377-82. PMC: 1746657. DOI: 10.1136/thorax.58.5.377. View

McGlynn E, Asch S, Adams J, Keesey J, Hicks J, DeCristofaro A . The quality of health care delivered to adults in the United States. N Engl J Med. 2003; 348(26):2635-45. DOI: 10.1056/NEJMsa022615. View

Knaus W, Draper E, Wagner D, Zimmerman J . APACHE II: a severity of disease classification system. Crit Care Med. 1985; 13(10):818-29. View

10.

Stiell I, Greenberg G, McKnight R, Nair R, McDowell I, Worthington J . A study to develop clinical decision rules for the use of radiography in acute ankle injuries. Ann Emerg Med. 1992; 21(4):384-90. DOI: 10.1016/s0196-0644(05)82656-3. View

11.

. Evidence-based medicine. A new approach to teaching the practice of medicine. JAMA. 1992; 268(17):2420-5. DOI: 10.1001/jama.1992.03490170092032. View

12.

Stiell I, Wells G, Hoag R, Sivilotti M, Cacciotti T, Verbeek P . Implementation of the Ottawa Knee Rule for the use of radiography in acute knee injuries. JAMA. 1997; 278(23):2075-9. View

13.

Wells P, Anderson D, Bormanis J, Guy F, Mitchell M, Gray L . Value of assessment of pretest probability of deep-vein thrombosis in clinical management. Lancet. 1998; 350(9094):1795-8. DOI: 10.1016/S0140-6736(97)08140-3. View

14.

Hoffman J, Wolfson A, Todd K, Mower W . Selective cervical spine radiography in blunt trauma: methodology of the National Emergency X-Radiography Utilization Study (NEXUS). Ann Emerg Med. 1998; 32(4):461-9. DOI: 10.1016/s0196-0644(98)70176-3. View

15.

Boersma E, Kertai M, Schouten O, Bax J, Noordzij P, Steyerberg E . Perioperative cardiovascular mortality in noncardiac surgery: validation of the Lee cardiac risk index. Am J Med. 2005; 118(10):1134-41. DOI: 10.1016/j.amjmed.2005.01.064. View

16.

Quinn J, McDermott D, Stiell I, Kohn M, Wells G . Prospective validation of the San Francisco Syncope Rule to predict patients with serious outcomes. Ann Emerg Med. 2006; 47(5):448-54. DOI: 10.1016/j.annemergmed.2005.11.019. View

17.

Lo G, Juhl D, Warkentin T, Sigouin C, Eichler P, Greinacher A . Evaluation of pretest clinical score (4 T's) for the diagnosis of heparin-induced thrombocytopenia in two clinical settings. J Thromb Haemost. 2006; 4(4):759-65. DOI: 10.1111/j.1538-7836.2006.01787.x. View

18.

Klompas M . Does this patient have ventilator-associated pneumonia?. JAMA. 2007; 297(14):1583-93. DOI: 10.1001/jama.297.14.1583. View

19.

Kline J, Mitchell A, Kabrhel C, Richman P, Courtney D . Clinical criteria to prevent unnecessary diagnostic testing in emergency department patients with suspected pulmonary embolism. J Thromb Haemost. 2004; 2(8):1247-55. DOI: 10.1111/j.1538-7836.2004.00790.x. View

20.

Ranson J, Rifkind K, Roses D, Fink S, Eng K, SPENCER F . Prognostic signs and the role of operative management in acute pancreatitis. Surg Gynecol Obstet. 1974; 139(1):69-81. View