This document discusses using cataract surgery audit data captured as part of normal workflow to analyze outcomes and refine surgical processes. The data is captured in separate pre-operation, operation, and post-operation templates that tell the story of the cataract operation. The audit data from over 70 data points across different clinics and countries can be used for population research, refining surgical processes, and auditing surgeons. Standards are needed for defining data elements and expressing the data to allow international benchmarking and interoperability.

1. Cataract Audit as part of Workflow Michael Mair Timaru Eye Clinic, PO Box 319, Timaru, NZ mikemair@eyetech.co.nz Eric Light Gravity Computing Ltd, PO Box 24163, Abels, Hamilton, NZ info@gravitycomputing.co.nz

3. Cataract surgery is one of the most successful procedures in the whole of medical practice

5. cataract

6. cataract

7. Phaco emulsification

8. Coloured Lens Implant

9. We capture cataract audit data as part of normal work flow This data can be analysed and the results have a number of uses: population research e.g. identifying needy populations from their improvement and morbidity profiles refinement of the surgical process itself The audit and re-validation of surgeons

11. The segregation of the harvested cataract data into separate pre-operation, operation, and post-operation templates allows the automatic tagging of data.

13. The record system described herein is being used in 86 medical practices (over 900 individual licenses), and is found in four countries. The cataract data set has over seventy separate clinical data points, is a subset of the total ophthalmology management system data set: also contains administrative and financial management data points, and has over one thousand fields in total.

15. performs a ‘once per visit’ harvest of eye-care data points.

16. contains condition-specific templates - such as cataract - which are automatically populated with data from exams template already evoked at the same encounter.

19. The Operation Note Has data points for all the stages of the physical cataract operation Has check boxes for risk factors, both shared and local Has a free text box because so much of the ‘story’ is not structured data. Is an ‘attested’ document, and a medico-legal entity.

21. The Post Op screen Has all the data points required to describe a clients post-operative condition Is complete by the clinician and ancillary staff when the patient returns after surgery Has flag fields for shared post-operative risk factors Has user defined fields for other locally identified risk factors

23. The ‘sign off’ button The ‘sign off’ button in the post op template. facilitates the preparation of a corpus of data for audit. It is also an ‘attestation“ boundary, and makes the record of that procedure unable to be subsequently modified.

25. The definitions of data elements Come from the practice of Ophthalmology, and are chosen because their definition is obvious and non controversial. There is no universal data dictionary for Ophthalmology, and no standard for the expression of it. We would like to offers some suggestions!

26. Data entry is direct by the user or copied directly into the record from the technology employed. or paper templates can be filled at the time of surgery these are secondarily entered into the management system.

27. Visual acuity is an important variable which is measured before and after cataract surgery. It is a measure of the angular size that a perceived image subtends at the macula. Snellen 6/6 is 5 seconds of arc at 6M

29. Risk Factors named risk factors are listed, and at risk cases can be included or excluded from a data analyzer cohort by the use of simple check boxes. This list may be modified by the user, to reflect the beliefs and experience of different user groups. A longer list of risk factors might be shared with other groups such as those who are contributing to the UK National Data Set.

30. UK National Data Set

32. Operative – e.g. capsule rupture, vitrectomy

33. Post operative –e.g. endophthalmitis

34. cases which do incur these risk factors can be included or excluded from a data analyser cohort by the use of simple check boxes.

36. For example, ensuring that the visual acuity entered by the surgeon is either in a valid Snellen format (e.g. 6/18), or matches standard extensions (such as CF or HM).

40. Acuity outcome graphs

49. Capsule rupture and vitrectomy The reports simply count the occurrences of these complications of surgery, and present them as a percentage in the selected patient cohort. There are internationally recognized benchmarks for these risk factors.

51. Deviation from Target refractive outcome This audit measure evaluates how well we achieved the intended ‘glasses’ outcome Mostly we try to make the client see well without glasses Sometimes a client will need to have an outcome that balances the other eye Sometimes a client will choose a myopic outcome Deviation from ‘target’ subsumes these

52. 4.3. Deviation from target refraction

53. Refining the reports By date, by surgeon, by clinic Excluding or including risk factors Customization of risk factor lists.

54. Interoperability and Best Practice in Medical Audit The paper by Jaycock et. al. on the UK National Cataract Data Set has introduced some benchmarking standards. – 55,000 operations The tool we have developed can deliver all the measures needed to fully participate in this international benchmarking process. By providing a dataset ready for expression in a universal format, the project can contribute as a test case to the development of interoperability standards for data and benchmarking.

55. Data definition and Ontology Our data definitions are intrinsic to contemporary cataract surgery . The ontology changes over time and place, and will continue to evolve. At any one time a data harvest will be defined and structured by a model of the process it follows, and which is shared by a community of cataract surgeons

56. As soon as we have an internationally agreed way of expressing clinical categories and a shared implementation technology for them, then this software and data analyser could work on any cataract data.

57. Draft for Discussion April 2010

60. Excel The use of Microsoft Excel itself confers some of this potential interoperability, since all that participant applications need to do is to share data definitions and export in the standard Comma-Separated Values (CSV) format. However, although the CSV format is an open standard, Microsoft Excel is both proprietary and platform-dependant, and does not of itself supply a data model.

61. HL7 The HL7 standard supplies both a data model and a standard exchange unit, the CDA, which may be used to collate contributions to an audit coming from different applications. Can the data elements identified in this study be expressed in this standard? The CDA is going to be used by the GP to GP project, and if successful, will be the vehicle for Specialist to Specialist communications as well

62. The Clinical Statement pattern The Clinical Statement Domain provides an abstract model used for the harmonization of all similar objects within V3. The formal definition: "An expression of a discrete item of clinical (or clinically related) information that is recorded because of its relevance to the care of a patient.”

63. Alternatives to the RIM OpenEHR Semantic Web SDMX SNOWMED - and the Web2 challenge

64. Transparency and Best Practice The availability of immediate audit and transparency has interesting implications for medical practice generally and for its practitioners. Up-to-date audits could be made publicly available, and therefore help patients choose their surgeon. Alternatively, audit information could be kept private, and only made accessible to the surgeon concerned or authorized auditors.

65. Best Practice in Conducting Audits Concepts of ‘best practice’ will vary with the way the delivery of cataract surgery is organized. We the participants must drive the design and implementation of harmonious and efficient clinical and administrative processes.

66. The ability to work transparently does not itself tell us how we should best balance the demands of patient safety, administrative efficiency, and the autonomy and dignity of medical practice.