Clinical Decision Support Systems in the Care of Hospitalised Patients with Diabetes

1. Clinical Decision Support Systems in the Care of Hospitalised Patients with Diabetes Dr. Krish Nirantharakumar MBBS, MPH, MD, MFPH, MRCP Senior Clinical Lecturer, University of Birmingham Academic Public Health Consultant, Public Health England

2. Background • 15-20% hospitalised patients have a diagnosis of diabetes in UK and USA • Poor glucose control in in-patients is associated with: • Increased infection rates • Increased mortality • Increased length of stay (LOS) • Inpatient audits suggest significant prescription errors and suboptimal glycaemic control • Patients with diabetes concerned about the quality of care they receive • CDSS is one of many initiatives recommended by ADA and NHS Diabetes to improve care for patients with diabetes

3. Definitions • CPOE – Computerised Physician Order Entry • Clinical Software application • Designed for use by clinicians • Write patient orders electronically rather than paper • CDSS – Clinical Decision Support System • “A computer system that uses two or more patient data to generate case specific or encounter specific advice”

4. Clinical Decision Support Systems (CDSS) • Computerised clinical decision support systems (CDSS) are described as information systems designed to assist and improve clinical decision making • Often referred to individual care but same principles apply when caring for a population • These include: • Electronic alerts • Individual (I) • allergy, abnormal blood results, need for a specific medication • Population (P) • Eligible patients not having received flu vaccination, diabetes patients not on Statin etc • Electronic triggers • Naloxone for excess opioid prescription (I/P) • Electronic guidelines (I) • Electronic prediction models (I/P)

5. Today’s talk………………. • Present findings of my doctoral research • Discuss opportunities to incorporate some of the work and other potential tools as part of PICS • Get your opinion on an OpenClinical.net project around diabetes care

6. Aims of the project • CDSSs in the care of inpatients with diabetes in non-critical care setting: systematic review • Epidemiological studies • Missing discharge diagnostic codes for diabetes • Association between hypoglycaemia and inpatient mortality & length of stay • Association between foot disease and inpatient mortality & length of stay • Hypoglycaemia in non-diabetic in-patients: clinical or criminal? • A prediction model for adverse outcome in hospitalised patients with diabetes

7. Computerised Physician Order Entry (CPOE) • Includes • Electronic prescriptions • Insulin order template • Modification to insulin order in line with guidelines • Alerts to encourage “efficient” use of insulin • Benefits • Compliance to guidelines of prescriptions • Higher use of basal bolus regimen • Avoidance of sliding scale insulin • Better glycaemic control • (0.6-0.8mmol/l patient day weighted mean blood glucose) • Possible reduction in length of stay • Reduces errors in prescriptions

8. Connective Technology for POC Blood Glucose Monitoring • POC blood glucose automatically transferred to central information system in real time • Benefits • As a marker of quality of care using “Glucometrics” • Ability to describe glucose control in wards, institutions, different time periods... • Improved blood glucose control • Active case finding • Surveillance

9. Glucometrics

10. Active Case Finding • Real-time case finding of patients in need of specialist input using health information system • Out of range glucose results (lab, POC) • Hypo / hyper • Patients on insulin infusions • Parenteral or enteral nutrition • Patients at high risk of adverse outcome (prolonged length of stay / mortality)

12. Aims of the project • CDSSs in the care of inpatients with diabetes in non-critical care setting: systematic review • Epidemiological studies • Missing discharge diagnostic codes for diabetes • Association between hypoglycaemia and inpatient mortality & length of stay • Association between foot disease and inpatient mortality & length of stay • Hypoglycaemia in non-diabetic in-patients: clinical or criminal? • A prediction model for adverse outcome in hospitalised patients with diabetes

13. Finding ‘lost discharge codes for diabetes’ • Under-reporting • Scotland - Primary care data to hospital data – 41% • England - Hospital Episode Statistics to National diabetes audit – 33% • Aim • To estimate the frequency of missed discharge diagnostic codes for diabetes using inpatient electronic prescription • To look at the feasibility of this approach in real-time correction • To estimate the impact it would have on diabetes-related payments to the hospital Trust

14. Methods • Linked PAS to PICS • Definition of diabetes • PAS E10-E14 • PICS - Anti-diabetic medication • Excluding – Metformin alone in PCOS and short acting insulin prescriptions alone • Cost calculation • By adding a diabetes diagnostic code & recalculating tariff using HRG v4 software PICS

15. Methods • Statistics • Estimating the frequency of missed diagnostic codes • Capture recapture method for two sources • Chao’s formula • Association between missed diagnosis and admission characteristics • Mixed effect logistic regression method

16. Results - Patients admitted with diabetes identified through discharge diagnostic code and electronic prescription data for 2007-2010

17. “Lost” diabetes diagnosis

18. Lost income • Change in HRG tariff code and payment was 1 in 8 only (347 out of 2,706) • Loss per patient with change in tariff £550

19. Proposed algorithm to incorporate into electronic prescription and health information system to reduce missed discharge diagnostic codes

20. Hypoglycaemia • Hospitalised patients with diabetes • Determine the association of hypoglycaemia with: • Mortality • Length of stay • Need for the study • Limited evidence from non critical care setting • Hospitalised non diabetic patients • Estimate the frequency of non diabetic hypoglycaemia • Determine if there is a plausible reason • Explore feasibility of surveillance • Need for the study • True frequency unknown • Useful in forensic cases • Patient safety

21. Presence and severity of hypoglycaemia • vs. length of stay and inpatient mortality

22. Presence of foot disease vs. inpatient mortality and length of stay

23. Aims of the project • CDSSs in the care of inpatients with diabetes in non-critical care setting: systematic review • Epidemiological studies • Missing discharge diagnostic codes for diabetes • Association between hypoglycaemia and inpatient mortality & length of stay • Association between foot disease and inpatient mortality & length of stay • Hypoglycaemia in non-diabetic in-patients: clinical or criminal? • A prediction model for adverse outcome in hospitalised patients with diabetes

24. Diabetes, inpatient mortality & length of stay • 602 deaths in two years in 13,794 patients with diabetes • 13.4% (95CI 4.5% -22.8%) more deaths • 81 preventable deaths

25. Diabetes, inpatient mortality & length of stay • SMR was 111.2 compared to those without diabetes • 11.2 % higher than non diabetic inpatients at UHB (95%CI: 3.1% to 18%) • Works out as 67 deaths How accurate are these? Are they useful?

26. Diabetes, inpatient mortality & length of stay • Length of stay at UHB • Results suggest: • Out of the 25,000 patients 10,000 have LOS less than or equal to expected median of non diabetic patients • 85% of excess LOS contributed by 25% of the patients

27. Diabetes, inpatient mortality & length of stay • Can we identify patients with adverse outcome (mortality and excessive length of stay) in the early phase of admission? Source: Clement S et al. Diabetes Care. 2004; 27:553-591[40]

28. Prediction model - Methods • Adverse outcome – composite outcome • Composite outcome – Death or excessive length of stay • Definition of “excessive” length of stay • Calculate median LOS of non diabetic patient for a given clinical condition (ND-los) • Excess LOS for every diabetes patient = LOS of diabetes patient – median of the ND-los for the same clinical condition • Excessive LOS = Patients above the 75th centile • Model variables – Include demographic details, admission method, place of admission, presence of foot disease, insulin use and blood results

29. Methods • Statistics • Model development • Multiple imputation • Generalised estimate equations • Logistic regression • Internal validation (using bootstrap method) • Discrimination – ability to differentiate (ROC curve, sensitivity, PPV etc) • Calibration – observed probability Vs. predicted probability

30. Predictors

31. ROC curve for model comparison & discrimination

32. Assessment of discrimination

33. Calibration plot

34. Today’s talk………………. • Present findings of my doctoral research • Discuss opportunities to incorporate some of the work and other potential tools as part of PICS • Get your opinion on an OpenClinical.net project around diabetes care

35. PICS and CDSS for diabetes

36. Today’s talk………………. • Present findings of my doctoral research • Discuss opportunities to incorporate some of the work and other potential tools as part of PICS • Get your opinion on an OpenClinical.net project around diabetes care

37. OpenClinical.net

38. CDSS - First generation Aides memoire for busy clinicians, making decisions about a specific patient, at a particular moment • Checks, alerts, reminders; • Prescribing and drug interactions; • Intelligent order entry Also • Text snippets; • Calculators; • Search engines (Cimino’s “infobuttons”);

39. CDSS -Second generation • First generation capabilities plus • Evidence-based recommendations for care • Based on practice guidelines • Embedded in workflow/care pathway • Explicitly manage clinical uncertainty and ambiguity • Examples: triage, risk assessments, diagnoses, tests and investigations, treatments etc • Specialised techniques: task modelling languages • ASBRU, EON, GLIF, PROforma, …

40. Knowledge management (AACE example) Clinical guidelines Research & reviews Evidence manager

41. Third generation • 3G technologies will focus increasingly on integrated care, addressing multidisciplinary, multi-morbidity and cross-sector care • They will support quality throughout the patient journey • Assist coordinated decision-making for clinicians • Automate as much “grunt work” as possible • Empower self-care by patients • Still research: 3G will require • much more flexible decision-making and careflow models • ability to use knowledge resources from many providers

42. Acknowledgement • Dr Jamie Coleman, UoB/ UHB • Dr Tom Marshall, UoB • Prof.John Fox, University of Oxford • Dr Parth Narendran, UoB/UHB • Dr Jonathan Webber, UHB • Dr Mujahid Saeed, UHB • Dr Amy Kennedy, UHB • Prof. R.E. Ferner, UoB • Prof. K.K. Cheng, UoB

43. Thank youQuestions?