Dr Yuen Wai-Cheung HA Convention 2011

Transcription

1 Dr Yuen Wai-Cheung HA Convention 2011

2 Outlines Why HA benchmarks hospitals? How to do a successful benchmarking? Using SOMIP as an example How to read and understand SOMIP report?

3 Benchmarking Benchmarking is the continuous search for significantly better practices that leads to superior performance, by investigating the performance and practices of other organisations (benchmark partners).

4 Benchmarking Industry Business management Health care Surgery

5 Why should HA benchmark hospitals? creates an atmosphere conducive to continuous improvement allows staff to visualise the improvement which can be a strong motivator for change challenges operational complacency helps to identify weak areas and indicates what needs to be done to improve creates a sense of urgency for improvement

6 What to benchmark in health care? Structure Process Outcomes

7 What to benchmark in Surgery? Structure Process Outcomes

8 Features of successful benchmarking Compare like for like Independence third party Executive involvement Use of reliable and current data Transparency

9 Australia Council on Health Care Standards: 16 standards for surgical outcomes Hospital mortality rate of elective abdominal aorta aneurysm repair Hospital mortality rate of elective Coronary artery bypass surgery Hospital mortality rate of elective Coronary artery bypass surgery for patients of age 71 or greater Stroke rate after carotid endarterectomy Bile duct injury rate after lap chole Blood loss after TURP Readmission rate after TURP Negative appendicectomy rate +ve margin rate after excision of skin tumour

10 Elective abdominal aortic aneurysm repair Numerator = Total number of patients having elective AAA repair performed, who die within the same admission. Denominator = Total number of patients having elective AAA repair performed. 20% percentile 3.45% 80% percentile 4.42% Simple to do But not comparing like to like

11 Their findings can be very wrong Because every surgical patient is different in terms of age, health status and disease condition, naturally the outcomes would be different, and it may not be related to quality of care Not convincing enough to ask professsionals like surgeons to change practice On one hand, they refuse to change On the other hand, they reject high risk patients

12 Relationship of surgical outcomes with quality of care is best summarized by Prof. Lisa Iezzoni Outcome = patient risk factors + effectiveness of care + random events

13 Relationship of surgical outcomes with quality of care is best summarized by Prof. Lisa Iezzoni Outcome = patient risk factors + effectiveness of care + random events

14 The problem is How? Risk- adjusted outcome measurement is much difficult to do Not many places in the World can achieve it

15 Surgical Outcomes Monitoring and Improvement Program (SOMIP) This program was evolved from the experience of six comparative audits undertaken in 2002 to 2007 by the Central Surgical Audit Unit of the Coordinating Committee of Surgery The program has gained full support from Head Office Quality and Safety Division and was launched in 2008 to all 17 hospitals with surgical departments The scope includes 24,000 major and ultramajor operations, elective and emergency each year The objective is to provide an apples to apples benchmarking of Surgical Departments

16 Data in SOMIP Outcome = patient risk factors + effectiveness of care + random events

17 Outcomes data Postoperation mortality within 30 days, 60 days and 90 days

18 19 Surgical complications within 30 days Major Surgical Complications Minor Surgical Complications Anastomotic leakage Surgical space infection Deep wound infection Wound dehiscence Intestinal obstruction requiring operation Acute limb ischaemia Flap failure Unexpected tissue injury Haematoma requiring intervention Upper GI bleeding requiring endoscopy Disseminated intravascular coagulation Septic shock Superficial wound infection Radiological anastomotic leakage Postop bleeding requiring > 4 units transfusion Prolonged paralytic ileus beyond postop Day 8 Sepsis Peripheral nerve injury

19 16 Medical complications within 30 days Major Myocardial infarction Stroke/CVA Coma > 24 hours Acute renal failure requiring dialysis Acute respiratory distress syndrome Unplanned intubation On ventilator over 48 hours Acute pulmonary edema Cardiac arrest Minor Pneumonia Aspiration pneumonia Pleural effusion requiring tapping or chest drain Pulmonary embolism Deep vein thrombosis Urinary tract infection Progressive renal failure not requiring dialysis

20 Data in SOMIP Outcome = patient risk factors + effectiveness of care + random events

21 What sort of risk factors to collect? Risk adjustment models for surgical patients

22 Physiological scoring systems for illness severity Variables Scope Acute Physiology and Chronic Health Evaluation (APACHE)II 18 ICU patients Simplified Acute Physiology Score (SAPS) 14 ICU patients Mortality predicting model (MPM)II 15 ICU patients Paediatric risk of mortality (PRISM) III 17 Paediatric ICU Trauma injury severity score (TRISS) 5 Trauma patients Physiological and Operative Severity Score for enumeration of Mortality and Morbidity (POSSUM) 18 Estimation of Physiologic Ability and Surgical Stress (E-PASS) 10 National Surgical Quality Improvement Program (NSQIP) 60 Surgical patients with operation Elective General Surgery Surgical patients with operation

23 Predictive power of risk adjustment model measured by C index Model C index for 30 day mortality POSSUM 0.74 EPASS 0.81 SOMIP/NSQIP 0.9 We adopted and modified the National Surgical Quality Improvement Program (NSQIP) model We expanded to 66 risk factors for each patient in SOMIP

24 General Medical Drugs Cardiovascular system Age Diabetes Mellitus Recent angina ASA Functional status Hypertension On steroid Recent myocardial infarction History of PTCA or cardiac surgery Respiratory System Severe COPD Preop dyspnoea Ventilator dependent Smoker Drinker Recent chemotherapy Recent radiotherapy Amputation for ischaemic limb Presence of limb gangrene/ rest pain Preop pneumonia BMI Psychosis Preop SBP Astrup O2 Disseminated malignancy Preop pulse Astrup CO2 DNR Preop sepsis Preop septic shock Presence of local wound

25 Central Nervous system Conscious level Hx of CVA or TIA Hemiplegia CNS tumour Hepatic system Hepatomegaly Ascites Esophageal varices Serum albumin Renal System Acute renal failure Renal failure requiring dialysis serum Sodium Serum Potassium Haematological system Uncorrected bleeding tendency Preop blood transfusion Haemoglobulin Haemotocrit Serum bilirubin Serum urea White cell count Alkaline phosphtase ALT Serum creatinine ph Base excess Platelet count Operative data Duration of operation Intraoperative blood loss Wound classification Operation magnitude Procedure complexity score

26 Current HA System Architecture Half of data are captured by IT

27 Processes to ensure data accuracy Operations captured by IT system (OTRS) Data definition manual for each variable Trained nurse reviewers Inter-rater variability test Cross-checking by clinicians and statisticians

28 Operations captured by OTRS Any operation with procedure weight >16 done in Surgical departments and recorded in Operation Theatre Record System (OTRS) are automatically captured on real-time basis Elective or emergency No miss

29 Data definition manual Acute myocardial infarction Myocardial infarction is defined as a typical rise and gradual fall (troponin) or more rapid rise and fall (CK-MB) of biochemical markers of myocardial necrosis with at least ONE of the following:- a. Ischemic symptoms b. Development of pathologic Q waves on the ECG c. ECG changes indicative of ischemia (ST segment elevation or depression) d. Coronary artery intervention (eg, angioplasty) If Acute coronary syndrome (ACS) is documented in the medical record TOGETHER with a typical increase in Tropinin or CK-MB, check Yes OR If AMI was documented by cardiologist / physician in the record with no other evidence of typical rise of cardiac enzyme, please confirm with surgical supervisor whether this is an AMI. Please note that suspected AMI or? AMI should not be counted.

30 6 trained full-time nurse reviewers To ensure consistency To ensure independence To ensure accuracy

31 How SOMIP obtains accurate and current data? Operations captured by IT system (OTRS) Data definition manual for each variable Trained nurse reviewers Inter-rater variability test 95% consistency Cross-checking by clinicians and statisticians

32 How SOMIP obtains accurate and current data? Operations captured by IT system (OTRS) Data definition manual for each variable Trained nurse reviewers Inter-rater variability test Cross-checking by clinicians and statisticians

33 After data processing, the data were sent for analysis

34 Data analysis by Statisticians 1. Analyse the relationship of risk factors and outcomes to select the independent factors out of the 66 variables 2. Construct predicting models for event by logistic regression method for all HA patients 3. Apply the model to each patient and calculate the probability of event of each patient and add up all the probabilities of each hospital 4. Calculate the O/E ratios and confidence interval for each hospital

35 SOMIP compares hospitals in term of O/E ratio Actual number recorded by nurse reviewer O/E Ratio = Observed number of events Expected number of events Theoretic number calculated by risk predicting model

36 SOMIP report Outcome = patient risk factors + effectiveness of care + random events

37 Confidence Interval Mortality 90% Major complication 95% Minor complication 99% SOMIP report 08-09

38 Prof.Iezzoni s formula Outcome = patient risk factors + effectiveness of care + random events

39 Prof.Iezzoni s formula Outcome = patient risk factors + effectiveness of care + random events

40 Prof.Iezzoni s formula Outcome = patient risk factors + effectiveness of care + random events

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42 Two parts in the report Risk adjusted benchmark of hospitals Overall ranking in terms of mortality and morbidity (medical, surgical, major, specific etc.) For head office and hospital executives use Crude outcome comparison of hospitals on 34 elective operations and 17 emergency operations For individual department, team and surgeon reference

43 Conclusions With the collaboration of HA head office, surgeons, nurse reviewers, IT experts and statisticians, HA has set up a robust mechanism to provide a prospective apples to apples benchmarking of surgical departments, using scientific statistical analysis of carefully captured patient s risk factors and outcomes and The findings are powerful to drive hospitals to improve

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46 Cost of SOMIP Annual cost about HK $ 3 Million Additional cost per operation = $125 per operation

47 Select appropriate surgical risk adjustment system Preop Risk scoring system American Society of Anesthesiologists (ASA) Physical Status Grading New York Heart Association (NYHA) classification of angina Canadian Cardiovascular Society (CCS) classification of cardiac status Goldman Cardiac Risk Index (CRI). Child s score or its Pugh s modification Shapiro scoring of pulmonary function Glasgow Coma Scale Organs all heart heart heart liver Lung Brain

48 Changes over two years

49 Only risk adjustment is age Hospital mortality rate of elective Coronary artery bypass surgery 20% percentile 1.24%, 80% percentile 1.39% Hospital mortality rate of elective Coronary artery bypass surgery for patients of age 71 or greater 20% percentile 2.02%, 80% percentile 2.99%

50 Elective C index 30 day mortality day mortality day mortality morbidity overall major morbidities major surgical morbidities major medical morbidities SOMIP Emergency C index 30 day mortality morbidity overall major morbidities major surgical morbidities major medical morbidities 0.9

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