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Use of intelligence by EPSO member states Dr Alex Mears Care Quality Commission.

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Presentation on theme: "Use of intelligence by EPSO member states Dr Alex Mears Care Quality Commission."— Presentation transcript:

1 Use of intelligence by EPSO member states Dr Alex Mears Care Quality Commission

2 2 The Care Quality Commission The Care Quality Commission is the regulator for England Created 1 st April 2009 (just over a year old) Remit covers all aspects of health (NHS and privately owned) and adult social care Does not include medical or clinical personnel (other bodies do that) Registration-based model- initial registration then ongoing monitoring of compliance Information is delivered to field staff through statistical risk model into the Quality and Risk Profile (QRP), indicating risk level for a number of outcomes for each service/ provider QRP used by field staff to prioritise regulatory activity QRP uses information from many sources including users of services

3 3 The project- background Different organisations within EPSO undertake different roles with different objectives. This project questionnaire looks at how our organisations use intelligence, information and/or data as part of regulatory/ supervisory activity. Some organisations will not use information very much, relying on a full-coverage model (where all providers of care are inspected on a rotational basis); others will use information to select or prioritise inspection activity. This questionnaire aims to understand how supervisory bodies in EPSO use information, and how this is linked to their aims and goals and other factors such as how they interact with supervised organisations

4 4 Methods This questionnaire was sent out by the EPSO central secretariat to representatives of all nations active within EPSO, regardless of membership status, comprising 18 countries in total. Two reminders were sent out to participants, each with a fresh copy of the questionnaire attached. The questionnaire was developed to enable the researchers to understand more about a number of areas of regulation. Sections gathered data on Demographics regulatory approach availability of information analysis capability regulatory model use of information. Analyses run were descriptive (univariate) and cross-tabulation (bi- or multi-variate).

5 5 Results: structure Results are presented using a structured approach (this was a long questionnaire and there are lots to show)] Viable responses were received from 12 nations of the 18 that received it, an adjusted response rate of 67% (some countries were not included in the original mail out and were added later). Analyses are presented as descriptives (univariate) and crosstabulations (bi- and multi-variate) to show relationships. The results presented do not represent the totality of data collected, but the highlights of analyses so far. V. small dataset means findings are only illustrative.

6 6 Demographics

7 7 Organisation status - Most organisations have no relationship with local government

8 8 Entry to and exit from the market

9 9 Service failure and economic factors - Most supervisors investigate service failure (10/12)

10 10 Relations with supervised bodies  All respondents reported a good relationship with supervised organisations

11 11 Reporting

12 12 Team members & recruitment

13 13 Research, mergers, change trends and finance Some organisations commission independent research from third parties (7/12) Most respondents feel that changes in their work follow a direction of travel (9/12) Half of respondents have been involved in mergers

14 14 Breadth of supervision

15 15 Powers

16 16 Data available for supervision 1

17 17 Data available for supervision 2

18 18 Data available for supervision 3

19 19 Data available for supervision 4

20 20 How information is used

21 21 How data drives inspection

22 22 Crosstabulations Looked at relationships between different variables created categories to explore the data Countries: Eastern Europe, Western Europe, Nordic Breadth of supervision: high, low Powers available: many, few Data use: basic to complex including risk-based Inspection model: driven by defined period to risk based Risk-model: hybrid of data use and inspection model

23 23 Breadth of supervision activity by geographical area -Eastern European supervisors’ scope is relatively smaller -the Nordic scope is relatively wider -Western Europe is between

24 24 Powers available by geographical region - Western Europe has comparatively more powers - Nordic countries have less powers - Eastern European countries are between

25 25 Availability of data by geographical area

26 26 Cluster chart of data available, powers and geographical region - More data is associated with more powers -This effect is most pronounced for Eastern European countries

27 27 Data sources available by powers available

28 28 Breadth of supervision by powers available

29 29 Inspection frequency by most sophisticated form of analyses - There appears to be a loose relationship between responsiveness of model and sophistication of analysis - These two variables can be combined to form a proxy measure for how risk-based a regulator is

30 30 Risk based model by geographical area

31 31 Risk based model by analyses used - There appears to be a loose relationship between risk-based models of regulation and total use of analyses

32 32 Risk based model by data available - There appears to be a loose relationship between risk-based models of regulation and availability of data

33 33 Risk based model by breadth of supervision - risk-based regulation is loosely associated with a smaller regulatory scope

34 34 Much more data to analyse! I’ll stop there, as we will have no time for discussion There are plans to complete the analyses, look for more associations and unpack the more qualitative information Plan to write and submit a journal article, probably to the International Journal of Quality in Health Care Open to the floor: invite comments, thoughts, opinion from delegates Questions for discussion: Does what the data show make sense? Are there relationships that are expected? Are there relationships that are surprising? Are there discernable patterns in the data? Are there other questions we should be asking? What other analyses should we be doing?

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