IS AUTOMATIC DATA COLLECTION FOR QUALITY INDICATORS POSSIBLE?

1. IS AUTOMATIC DATA COLLECTION FOR QUALITY INDICATORS POSSIBLE? 17.3.2011 Matti Reinikainen North Karelia Central Hospital Joensuu, Finland

2. CONFLICTS OF INTEREST STATEMENT: • MATTI REINIKAINEN, MD • - Position: Chief Physician, Dept of Intensive Care, North Karelia Central Hospital, Joensuu, Finland • - Position of responsibility: 1st Secretary, Finnish Society of Intensive Care • Connection with the Finnish Intensive Care Consortium: • Using reports and analyses as department leader • Using database for research purposes, PhD thesis under preparation • Economic interests in this subject: none

3. TERMS USED IN THIS PRESENTATION Clinical information system (CIS) a computer system that replaces bedside paper documentation the system automatically collects data from patient monitors and ventilators and the lab system and shows the data both numerically and as graphic trends bedside screen(s) Data collection software a computer system that is linked to the CIS and automatically transfers data to the centralised database of the Finnish Intensive Care Consortium data accuracy is checked before submission

4. QUESTIONS TO BE ANSWERED • If automatic data collection were possible, would there be associated benefits? • Is it possible? • Are there drawbacks?

5. When you are taking care of a patient who is bleeding

6. …and who is haemodynamically unstable

7. Do you have time for careful documen-tation of blood pressures etc.?

8. Or would it be helpful if the data were collected automatically?

9. Automatic data capture into a clinical information system decreases the time spent by nurses on documentation and increases the time spent on patient care Wong DH et al. Changes in intensive care unit nurse task activity after installation of a third-generation intensive care unit information system. Crit Care Med 2003; 31: 2488-94. Before and after installation of clinical information system The percentage of time ICU nurses spent on documentation decreased by > 30% and the time spent on patient care increased

10. Automatic data capture into a clinical information system decreases the time spent by nurses on documentation and increases the time spent on patient care Bosman RJ et al. Intensive care information system reduces documentation time of the nurses after cardiothoracic surgery. Intensive Care Med 2003; 29: 83-90. Randomised controlled trial! – documentation on paper vs. into an information system A 30% reduction in documentation time (p < 0.001) was achieved, corresponding to 29 min per 8 h nursing shift This time was completely re-allocated to patient care

11. … or does it? Saarinen K, Aho M. Does the implementation of a clinical information system decrease the time intensive care nurses spend on documentation of care? Acta Anaesthesiol Scand 2005; 49: 62-5. After the implementation of a CIS, there was a small (statistically non-significant) increase in the time spent on documentation However, simultaneously there was a significant increase in the time spent on patient care ”…any plans to reduce the ICU staff with the aid of computers were not justified.”

12. Finland

13. Finland North Karelia Central Hospital Pirjo Kontio Population in the district: 173 000 (+ 200-300 bears)

15. FINLAND • Population 5,3 million • Area 338 000 km2

16. The Finnish Intensive Care Study, 1986-87 - 25 ICUs - Niskanen M, Kari A, Halonen P. Five-year survival after intensive care – comparison of 12 180 patients with the general population. Crit Care Med 1996: 24: 1962-1967. • The Severity Study - Le Gall J-R et al. A new Simplified Acute Physiology Score (SAPS II) Based on a European / North American Multicenter Study. JAMA 1993: 270: 2957 - 13 152 patients (720 from 7 Finnish hospitals) - Aarno Kari as country coordinator

17. THE FINNISH INTENSIVE CARE CONSORTIUM - A quality assurance project started in 1994 - Strong growth since 1998 - university hospitals joined in 2000-2002 1994

18. THE FINNISH INTENSIVE CARE CONSORTIUM 1994 2007

19. 20 hospital districts on Finnish mainland • The main hospital in each district is called the Central hospital • 15 non-university hospitals, all adult ICUs participate in the Consortium • 5 university hospitals • All ICUs from 3 of these participate • In 2 university hospitals: in addition to participating units, some specialised units not participating • Apart from 1 ICU, all units use clinical information systems and automatic data transfer into a centralised database

20. Data collected by clinical information systems, including laboratory test results

21. … are automatically transferred by a data collection software to the centralised database of the Finnish Intensive Care Consortium • The database is handled by Tieto Healthcare & Welfare (previously by Intensium) • The results in key performance indicators are calculated and reported, many of them automatically

22. QUALITY INDICATORS • DATA COMPLETENESS • ADEQUACY OF PATIENT SELECTION • OUTCOMES • RESOURCE CONSUMPTION

23. EXAMPLES OF REPORTS THAT ARE UPDATED (SEMI)AUTOMATICALLY In the following slides: • Blue squares = North Karelia Central Hospital • Red circles = the rest of the Finnish Intensive Care Consortium (i.e. ”the average ICU”) • Each square / circle is based on data from the previous 6 months

24. DATA COMPLETENESS The data completeness index: the second best performing unit (next to the one with the least missing data) gets the index figure 100. The second worst performer (next to the one with most missing data) gets the index figure 50. Other units get index figures based on how close their performance is to these two.

25. ADEQUACY OF PATIENT SELECTION • Basic idea: the indication for intensive care is a temporary danger to life and a possibility to prevent death by intensive care • ICU admissions may be ”inadequate” when • there is no danger to life and no care of high intensity is needed – could these patients be managed elsewhere? • patients are moribund – care is futile

26. High risk and intensive care The percentage of patients with a high risk of death (> 0,3) and a high intensity of care (TISS score > 30/d)

27. Low risk – unnecessary ICU admission? The percentage of patients who had a low risk of death (< 0,05), received care of low intensity (maximal TISS score < 15/d) and were discharge alive

28. OUTCOMES • Hospital mortality rate (crude & standardised mortality ratios) • Mortality after long ICU stays • Post-icu mortality • (Also measured, though with a considerable amount of manual work: 6-month mortality & health-related quality of life at 6 months)

29. VLAD curve, the cumulative amount of ”extra lives saved”; curves for 3 ICUs

32. SMR Standardised mortality ratios (O/E-ratio, the number of observed deaths divided by the number of expected deaths, the expected number here being based on the SAPS II model)

33. Hospital mortality after prolonged ICU care Hospital mortality of patients who were treated in the ICU for > 6 days

34. Prolonged care – poor outcome The percentage of patients who were treated in the ICU for > 6 days and who died in the ICU

35. Post-ICU hospital mortality A problem here? The percentage of patients who died in hospital after discharge from ICU

36. Readmissions within 48 hrs after discharge Shortage of beds? The percentage of patients who were readmitted to the ICU within 48 hrs after ICU discharge

37. RESOURCE CONSUMPTION • In relation to care days produced • (Also measured: ”Cost of lives saved” – the amount of resources consumed per hospital survivor. However, comparisons are difficult because all ICU costs are not easily obtained in Finnish hospitals.)

38. Care days / nurse / day The number of patient days (24-h-periods) per each nurse / shift

39. IN PRACTICE, WE CAN SPEAK ABOUT SEMI-AUTOMATED DATA COLLECTION Many data are entered manually into the clinical information system Admission data ICU and hospital discharge data (incl. outcome) TISS items are documented partly automatically, partly manually Some physiological data need to be entered manually

41. Even automatically collected data are checked and validated, … in North Karelia by this team

42. Admission data are checked for accuracy and for missing data

43. TISS items are checked

44. Lab test results are mostly transferred without problems

45. Some derived parameters can be problematic (mmHg) The PaO2/FIO2-ratio is correct only if both values are documented correctly FIO2 is documented automatically but monitoring may not be on e.g. when inhalational drugs are given

46. Values of some physiological parameters have to be checked for possible technical artifacts • e.g. blood pressure (trends of systolic BP in the next examples)

47. Patient 1 Unfiltered raw data

48. Patient 1 Median filtering (10 min) eliminates most technical artifacts

49. Patient 2 Unfiltered raw data

50. Patient 2 Median filtering eliminates most technical artifacts – but not all of them