DOI: http://dx.doi.org/10.18203/2320-6012.ijrms20162953

Data directed root cause analyses of hospital adversities and their proximities

Ramalingam Shanmugam

Abstract


Background: In this current era of healthcare reformations, medical professionals, patients, governments, and insurance agencies seek zero tolerance with respect to adverse outcomes. When adversities occur, hospital administrators more often than not perform root cause analysis (RCA) to avoid future reoccurrence. There exist three types of RCA. They are divergent, serial, and convergent root causes. Which adversity type exists in a situation is not medically or intuitivcly trivial. This article develops a data directed new methodology to characterize the type and interpret it.

Methods: Because tracing root causes of medical adversity is a necessity, pertinent data are collected. Patterns in correlation are examined to check whether it is a divergent or serial type. When it is not either, it is concluded to be convergent. This practice is too elementary to convince professionals. For this purpose, this article innovatively develops a new methodology using inverted correlation matrix and Mahalanobis distances to sort out causes of adversity as serial, divergent, or convergent type. Their proximities are quantifiable due to new expressions in the article. These expressions are not seen in the literature and hence, would benefit practioners.

Results: A new methodology of this article is illustrated using medical adversities that existed in hospitals during 2006 through 2014 in Indiana state. Data consist of number of surgeries, cases with ulcer acquired in hospital, cases with foreign objects in patient after surgery, cases with wrong part surgery, deaths due to medication error, and disability cases due to fall during hospital treatment. Their correlations ranged from -0.87 to 0.79.  

Conclusions: This article has developed expressions to quantify non-equilibrium level in serial and divergent RCA and has demonstrated their use to identify a convergent RCA. The Mahalanobis distance of attained diversities from an ideal scenario is obtained. A formula to make and interpret safety index is developed and demonstrated using adversities that occurred in India State during 2006-2014. These concepts and analytic expressions would enrich the practice of RCA which is a necessity in the current era of healthcare reforms.

 


Keywords


Serial, Divergent, Convergent root causes

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References


Whitson T, Garten B, Ordway, GV. Indiana medical error reporting system: A final report for 2014, Source: www.in.gov/isdh. 2015.

Shanmugam R. Probabilistic health-informatics and bioterrorism, International Journal of Communication and Computer. 2013;10:28-32.

Knudsen P, Herborg H, Mortensin AR, Knudsen M, Hellebek A. Preventing medication errors in community pharmacy: root-cause analysis of transcription errors. Quality Safety Health Care. 2007;16:285-90.

Song L, Chila N. Risk analysis of falls in an assisted living community. Quality Management Health Care. 2007;16(4):335-40.

Rooney J, Vanden-Heuvel L. RCA for beginners, Quality Progress. 2004;45-53.

Zegers M, de Bruijne, MC, de Keizer B, Merten H, Groenewegen, P, van der Wal G, Wagner C. The incidence, root-causes, and outcomes of adverse events in surgical units: implication for potential prevention strategies. Patient Safety in Surgery. 2011;5(13):1-11.

Schafer JJ. RCA project in a medication safety course, American Journal of Pharm Educ. 2012;76(6):116.

Fisher RA. The distribution of the partial correlation coefficient. Metron. 1924;3 (3–4):329-32.

Kunihiro B, Shibata R, Sibuya M. Partial correlation and conditional correlation as measures of conditional independence. Australian and New Zealand Journal of Statistics. 2004;46(4):657-64.

Alemi F. Measuring uncertainty. In Decision Analysis for Healthcare Managers, edited by Alemi, F. and Gustafson, D., Health Administration Press, Chicago, IL.. 2006;71-95.

Alemi F. Probabilistic risk assessment is practical. Quality Management Health Care. 2007;16(4):299-309.

Mahalanobis PC. On the generalized distance in statistics, Proceedings of the National Institute of Sciences of India. 1936;2(1):49-55.

Hover C, O’Donnel L. Probabilistic risk analysis of medication error. Quality Management Health Care. 2007;16(4):383-8.