Harnessing data from electronic medical records to improve patient care and health efficiency A proof-of-concept in paediatric oncology Jane Shrapnel Data Scientist, Sydney Children’s Hospital Network October, 2018
A day in the life of a doctor: the technical story 6 hours a day are spent using 207 computers Other • 2 hours updating eMR, including 29% note writing 148 Patient • 1 hour looking up and assessing 21% patient data 365 Computer Doctors are spending a small amount 51% of time with patients: • 1 and half hours in rounds • 1 hour with patients Minutes % of day Role of a doctor is to apply the principles and procedures of medicine to prevent , diagnose , care for and treat patients with illness, disease and injury and to maintain physical and mental health. Mamykina , L et al. ‘ How Do Residents Spend Their Shift Time? A Time and Motion Study With a Particular Focus on the Use of Computers’, Acad Med 2016 Definition of medical doctor from the International Labour Organisation
How can we harness the value of this data while assisting clinicians to provide high levels of care? Three problems to solve: Have real-time access to all data within the electronic medical records 1 without impacting the production system Utilise the vast quantities of information to attain value through improved 2 patient care and increased health efficiency Display these insight in the eMR to inform clinicians decision making at the 3 right time in their workflow
2 Real-time access to electronic medical records Benefits: eMR Data • Large quantities of data can accessed any time as no impact of production Product- Mirrored performance ion Copy • In real-time • Apply complex queries on structured and unstructured data Clinicians Blood enters blood results are test results at updated at 12:01pm 12:01pm
How can we harness the value of this data while assisting clinicians to provide high levels of care? Three problems to solve: Have real-time access to all data within the electronic medical records 1 without impacting the production system Utilise the vast quantities of information to attain value through improved 2 patient care and increased health efficiency Display these insight in the eMR to inform clinicians decision making at the 3 right time in their workflow
2 Harnessing value from the eMR is complex The eMR database contains all the encounters that a patient has had with the hospital as well as unnecessarily information for analysis. This information includes: • Recording vital parameters & diagnostic tests • Treatments including procedures and medication prescribed • All notes recorded for a patient • Transactional information such as system changes and system rules 70% of this data is unstructured, in the form of images or text The structured data requires quality assessment and rules for cleaning and interpretation Given this complexity, how to we go about showing we can extract value?
2 Start with a proof of concept to show impact Question: how can we improving the model of care for oncology patients that present with a fever and a low white blood cell count, known as febrile neutropenia? • Validate internationally recognised clinical decision rules • These patients are at a higher risk of adverse events resulting from a serious infection as they are immune compromised 1 • However, only 9% have a serious infection so there is room to change model of care to home care for those at low risk of infection • Currently treatment means immediate antibiotics and at least 48 hours in hospital Potential Impact: • Reduce costs • Higher quality of life • Patients and families spending less time in hospital • Free up critical bed space 1. Haeusler GM, Thursky KA, Slavin MA et al. (2017) External validation of six paediatric fever and neutropenia clinical decision rules. Pediatric Infectious Disease Journal
2 Internationally published risk stratification models: PICNICC and SPOG The Predicting Infectious Complications in Children with Cancer (PICNICC) study 1 and the Swiss Paediatric Oncology Group 1 (SPOG) study 2 have developed multivariate regression models to predict if a patient is at risk of contracting an infection. The table 2 below details the similarities and differences between the studies. PICNICC SPOG Risk of microbiological 3 Outcome What the model is trying to predict Risk of adverse outcome defined infection Prediction rate of infection out of 10 patients Model performance Number of countries data used to develop model 15 2 Time period to Predicted at admission x predict Predicted within 24 hours of admission x outcome Additionally, current Australian research shows promising results. Validating seven validations rule in retrospective study showed: • SPOG rule showed sensitivity of 78% and specificity of 46.3% 4 • PICNICC showed sensitivity 78.4% and specificity of 39.8% 5 PICNICC: The Australian predicting infectious complications in children with cancer project: • Prospective observational study, over 8 paediatric hospitals sampling 845 children • Validation of a large number of clinical decision rules 1 Phillips RS, Sung L, Amman, RA et al. (2016) Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participants data multivariable meta-analysis. British Journal of Cancer 2 Ammann RA, Bodmer N, Hirt A et al. (2010) Predicting adverse events in children with fever and chemotherapy-induced neutropenia: the prospective multicentre SPOG 2003 FN study. J Clin Oncol 28 (12) 3 Adverse outcomes include serious medical complication, microbiological defined infection, radiologically confirmed pneumonia 4 Haeusler GM, Thursky KA, Slavin MA et al. (2017) External validation of six paediatric fever and neutropenia clinical decision rules. Pediatric Infectious Disease Journal 5 Haeusler GM, Thursky KA et al. (2017) Predicting Infectious Complications in Children with Cancer: an external validation study. British Journal of Cancer
2 Defining the cohort in the eMR Defining febrile neutropenia patients : • Admitted to Oncology speciality Temperature of >= 38.0 o C & neutrophil count < 1.0 X 10 9 cells/L within first 24 hours • • V isit reason includes reference to a ‘fever’ or ‘febrile’ in lieu of a high temperature Final sample: Date range is from 3 April – 31 December 2017 for Children’s Hospital Westmead’s data 1 • 202 encounters • At admission: 155 encounters 2 • At day 2: 129 encounters • 98 patients 1 Children’s Hospital Westmead went live for online between the flags from 3 April 2017 2 If any predictor values were missing, these individuals were removed from the analysis
2 Variable creation required Source data to validate clinical decisions rules Variable Quality Source Neutropenia ANC of < 1.0 X 10 9 cells/L Criteria for Medium Neutrophil count inclusion in Fever of ≥ 38.0 o C Medium Temperature CDR Preceding chemotherapy more Unknown Named protocol intensive than ALL maintenance SPOG Haemoglobin ≥90 G/L Medium Haemoglobin CDR Total white cell count < 0.3 G/L Medium White Cell Count Platelet <50 G/L Medium Platelet count Clinical description ’severely unwell' Unknown RR & clinical notes Total white cell count (X109/L) Medium White Cell Count Temperature ( C) PICNICC CDR Medium Temperature Haemoglobin (g/L) Medium Haemoglobin Absolute monocyte count (X109 /L) Medium Monocyte count Malignancy type Low Active problems Microbiologically Positive bacterial High Microbiology report defined infection (MDI) Quality scale Low – high amounts of missing data, high potential for human error Medium – low amounts of missing data, small to medium potential for human error High – No missing data, small potential for human error (DQ steps in place)
2 Categorising MDIs from pathology results required development of a classification algorithm Microbiologically defined infection (MDI) is defined as an infection that is clinically detectable and microbiologically proven. Examples of what is in the report: • Code 0 – No growth at 5 days. • Code 1 - Staphylococcus epidermidis was isolated from the anaerobic bottle. Probable contaminant . Aerobic bottle • Code 2 - Two strains of Staphylococcus epidermidis was isolated from the aerobic bottle. • Code 3 – E.Coli was isolated Categorisation Coded value No MDI 0 Possible MDI, possible contaminant 1 Probable MDI 2 Confirmed MDI 3
2 Categorisation decision flow of MDI (bacteraemia) This categorisation reduces the hours a research nurse would spend reading the eMR and categorising the presence of an MDI Categorised as 1: Categorised as 2: Possible MDI, Probably MDI possible infection MDI is defined as an infection that was clinically detectable and microbiologically proven. For this study the focus is on Bacteraemia which is defined as a recognised pathogen from >= 1 blood culture or common commensals from >= 2 blood cultures drawn on separate occasions
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