Predicting a surgical site hospital acquired infection : a case study
| dc.contributor.advisor | Nieuwoudt, Isabelle | en_ZA |
| dc.contributor.author | Bartsch, Nicole | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. Logistics. | en_ZA |
| dc.date.accessioned | 2020-02-25T14:37:26Z | |
| dc.date.accessioned | 2020-04-28T12:28:35Z | |
| dc.date.available | 2020-02-25T14:37:26Z | |
| dc.date.available | 2020-04-28T12:28:35Z | |
| dc.date.issued | 2020-03 | |
| dc.description | Thesis (MCom)--Stellenbosch University, 2020. | en_ZA |
| dc.description.abstract | ENGLISH SUMMARY : In this study a logistic regression model for a private healthcare group, was used to determine the predicted number of Surgical Site Infections (SSIs) of an operative procedure at a healthcare facility. The purpose of this study is to determine the Standard Infection Ratio (SIR) which compares the actual number of SSIs that were contracted by patients at a hospital against the number of SSIs predicted. A SIR of above 1 is regarded as a bad result as the model predicted less infections to occur at a hospital than the actual number of infections that did occur. A SIR of below 1 is an ideal and good result that hospitals should aspire to achieve. The SIR is calculated across three hospitals, across three years (2016, 2017 and 2018) and across ve operative procedure groups (HYST, SB, BILI, CARD and KPRO). Speci c signi cant risk variables were taken into account per operative procedure group. These variables ranged from whether the patient was a diabetic or not, the age of the patient, which hospital the patient was admitted to, the BMI of the patient and the ASA score of the patient. Since the American Society of Anesthesiologists Classi cation (ASA) score is not captured electronically per patient, a logic was developed to determine the ASA score of a patient based on their clinical coding information and level of care they received. The logistic regression model was developed per operative procedure group and determines the probability of a patient contracting an SSI. A Hosmer-Lemmeshow goodness of t test was conducted to compare the actual events against the predicted events across 10 subgroups of the model's population. Finally, the SIR was calculated by dividing the actual number of SSIs by the predicted number of SSIs at a hospital. There is a clear di erence in the SIR results across the three hospitals that were considered, over the three years being analysed. Hospital A needs to focus on the operative procedure group CARD and Hospital B needs to focus on all ve operative procedures except for the operative procedure group SB where they scored an SIR of below 1. Hospital C achieved exceptional SIR results with all operative procedure groups across all three years having an SIR result of below 1. Both Hospital A and Hospital B need to improve the infection prevention and control practices at their hospitals as well as schedule interventions to decrease the number of SSIs occurring at their hospitals. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING : In hierdie studie is 'n logistieke regressiemodel vir die private gesondheidsorgeenheid, gesondheidsorgeenheid gebruik om die voorspelde aantal chirurgiese lokale infeksies (SSIs) na 'n operasie by een van gesondheidsorgeenheid se hospitale, te bepaal. Die doel van hierdie studie is om die Standaard Infeksie Verhouding (SIR) te bepaal wat die werklike aantal SSIs wat deur pasi ente in 'n hospitaal opgedoen is met die aantal voorspelde SSI's te vergelyk. 'n SIR van groter as 1 word as 'n slegte resultaat beskou, aangesien die model voorspel het dat minder infeksies in 'n hospitaal sou voorkom as die werklike aantal infeksies wat wel voorgekom het. 'n SIR van minder as 1 is 'n ideale en goeie resultaat waarna hospitale behoort te mik. Die SIR word bereken oor drie hospitale, oor drie jaar (2016, 2017 en 2018) en oor vyf operatiewe prosedure goepe (HYST, SB, BILI, CARD en KPRO). Spesi eke beduidende veranderlikes is per operatiewe prosedure groep in ag geneem. Hierdie veranderlikes het gewissel tussen of die pasi ent 'n diabeet was of nie, die ouderdom van die pasi ent, in watter hospitaal die pasi ent opgeneem is, die BMI van die pasi ent en die ASA-telling van die pasi ent. Aangesien die American Society of Anesthesiologists Classi cation (ASA) telling nie elektronies per pasi ent opgeneem word nie, is 'n logika ontwikkel om die ASA-telling van 'n pasi ent te bepaal op grond van hul kliniese koderingsinligting en die versorgingsvlak wat hulle ontvang het. Die logistieke regressiemodel is per operatiewe prosedure groep ontwikkel en bepaal die waarskynlikheid dat 'n pasi ent 'n SSI kan opdoen. 'n Hosmer-Lemmeshow geskiktheidstoets is uitgevoer. Uiteindelik is die SIR bereken deur die werklike aantal SSI's te deel deur die voorspelde aantal SSI's vir 'n hospitaal. Daar is 'n duidelike verskil in die SIR-resultate in die drie hospitale wat beskou is gedurende die drie jaar wat geanaliseer was. Hospitaal A moet fokus op die operasionele prosedure groep CARD en Hospital B moet fokus op al vyf operatiewe prosedure groepe, behalwe die operatiewe prosedure groep SB waar hulle 'n SIR van onder 1 behaal het. Hospital C het uitsonderlike SIRresultate behaal deurdat alle operasionele prosedure groepe gedurende al drie jare 'n SIR-uitslag van minder as 1 behaal het. Beide Hospitaal A en Hospitaal B moet klem l^e op die verbetering van infeksievoorkomings en beheerpraktyke by hul hospitale, sowel as intervensies bewerkstellig om die aantal SSI's wat by hul hospitale voorkom, te verminder. | af_ZA |
| dc.description.version | Masters | en_ZA |
| dc.format.extent | xii, 55 pages ; illustrations, includes annexure | |
| dc.identifier.uri | http://hdl.handle.net/10019.1/108261 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Surgery -- Complications -- Forecasting | en_ZA |
| dc.subject | Wounds and injuries -- Infections -- Forecasting | en_ZA |
| dc.subject | Health facilities, Proprietary -- Quantitative Management | en_ZA |
| dc.subject | UCTD | |
| dc.title | Predicting a surgical site hospital acquired infection : a case study | en_ZA |
| dc.type | Thesis | en_ZA |