Cataract Audit as part of Workflow


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Michael Mair
Timaru Eye Clinic
(3/11/10, Square, 1.30)

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Cataract Audit as part of Workflow

  1. 1. Cataract Audit as part of Workflow<br />Michael Mair<br />Timaru Eye Clinic, PO Box 319, Timaru, NZ<br /><br />Eric Light<br />Gravity Computing Ltd, PO Box 24163, Abels, Hamilton, NZ<br /><br />
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  3. 3. Cataract surgery is one of the most successful procedures in the whole of medical practice<br />
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  5. 5. cataract<br />
  6. 6. cataract<br />
  7. 7. Phaco emulsification<br />
  8. 8. Coloured Lens Implant<br />
  9. 9. We capture cataract audit data as part of normal work flow<br /> This data can be analysed and the results have a number of uses: <br />population research e.g. identifying needy populations from their improvement and morbidity profiles <br />refinement of the surgical process itself <br />The audit and re-validation of surgeons<br />
  10. 10. The cataract surgery experience has the structure of a classic ‘rite de passage’<br /><ul><li>The clinical procedure is structured into distinct pre-operation, operation, and post-operation stages.
  11. 11. The segregation of the harvested cataract data into separate pre-operation, operation, and post-operation templates allows the automatic tagging of data.
  12. 12. It also is faithful to the patient experience</li></li></ul><li>Story Boards<br />Data harvest for cataract surgery differs from that of medical eye disease such as ‘glaucoma’, where the record of visits may be reiterated, or ‘iritis’ which is episodic.<br />A sequence of templates is a ‘story-board’ in that it tells the story of a cataract operation. <br />The clinical story differs from disease to disease and often the story is unique!<br />
  13. 13. The record system described herein<br />is being used in 86 medical practices (over 900 individual licenses), and is found in four countries.<br />The cataract data set has over seventy separate clinical data points, <br />is a subset of the total ophthalmology management system data set: <br />also contains administrative and financial management data points, and has over one thousand fields in total. <br />
  14. 14. Method: The PMS<br /><ul><li>Has a data dictionary of ophthalmological concepts, and it
  15. 15. performs a ‘once per visit’ harvest of eye-care data points.
  16. 16. contains condition-specific templates - such as cataract - which are automatically populated with data from exams template already evoked at the same encounter.
  17. 17. uses data captured at the work face without additional transcription, and the audits and other results are always available and current. </li></li></ul><li>The Pre-op Screen<br />Has all the data points required to describe a clients pre-operative condition<br />Is complete by the clinician and ancillary staff when the patient is booked for surgery<br />Has flag fields for shared pre-operative risk factors<br />Has user defined fields for other locally identified risk factors<br />
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  19. 19. The Operation Note<br />Has data points for all the stages of the physical cataract operation<br />Has check boxes for risk factors, both shared and local<br />Has a free text box because so much of the ‘story’ is not structured data.<br />Is an ‘attested’ document, and a medico-legal entity.<br />
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  21. 21. The Post Op screen<br />Has all the data points required to describe a clients post-operative condition<br />Is complete by the clinician and ancillary staff when the patient returns after surgery<br />Has flag fields for shared post-operative risk factors<br />Has user defined fields for other locally identified risk factors<br />
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  23. 23. The ‘sign off’ button<br />The ‘sign off’ button in the post op template. facilitates the preparation of a corpus of data for audit. <br />It is also an ‘attestation“ boundary, and makes the record of that procedure unable to be subsequently modified.<br />
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  25. 25. The definitions of data elements<br />Come from the practice of Ophthalmology, and are chosen because their definition is obvious and non controversial.<br />There is no universal data dictionary for Ophthalmology, and no standard for the expression of it.<br />We would like to offers some suggestions!<br />
  26. 26. Data entry<br />is direct by the user or copied directly into the record from the technology employed.<br />or paper templates can be filled at the time of surgery<br />these are secondarily entered into the management system. <br />
  27. 27. Visual acuity<br />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. <br />Snellen 6/6 is 5 seconds of arc at 6M<br />
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  29. 29. Risk Factors<br />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. <br />This list may be modified by the user, to reflect the beliefs and experience of different user groups. <br /> A longer list of risk factors might be shared with other groups such as those who are contributing to the UK National Data Set. <br />
  30. 30. UK National Data Set<br />
  31. 31. <ul><li>Pre-operative – e.g. corneal opacity, macular pathology
  32. 32. Operative – e.g. capsule rupture, vitrectomy
  33. 33. Post operative –e.g. endophthalmitis
  34. 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.
  35. 35. This list is also modifiable by the user, to reflect the beliefs and experience of different user groups</li></li></ul><li>Data pre-processing<br /><ul><li>The pre-processing stage consists of a simple routine that ensures the validity of the data present in the export.
  36. 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).
  37. 37. Rows that do not hold valid data are removed from the processing set, and the user is alerted to their presence. </li></li></ul><li>
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  40. 40. Acuity outcome graphs<br />
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  49. 49. Capsule rupture and vitrectomy<br />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.<br />
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  51. 51. Deviation from Target refractive outcome<br />This audit measure evaluates how well we achieved the intended ‘glasses’ outcome<br />Mostly we try to make the client see well without glasses<br />Sometimes a client will need to have an outcome that balances the other eye<br />Sometimes a client will choose a myopic outcome<br />Deviation from ‘target’ subsumes these<br />
  52. 52. 4.3. Deviation from target refraction<br />
  53. 53.  Refining the reports<br />By date, by surgeon, by clinic<br />Excluding or including risk factors<br />Customization of risk factor lists.<br />
  54. 54. Interoperability and Best Practice in Medical Audit<br />The paper by Jaycock et. al. on the UK National Cataract Data Set has introduced some benchmarking standards. – 55,000 operations<br />The tool we have developed can deliver all the measures needed to fully participate in this international benchmarking process. <br />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. <br />
  55. 55. Data definition and Ontology<br />Our data definitions are intrinsic to contemporary cataract surgery .<br />The ontology changes over time and place, and will continue to evolve. <br />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<br />
  56. 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.<br />
  57. 57. Draft for Discussion<br />April 2010<br />
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  60. 60. Excel<br />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. <br /> 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.<br />
  61. 61. HL7<br />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.<br />Can the data elements identified in this study be expressed in this standard?<br />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<br />
  62. 62. The Clinical Statement pattern<br />The Clinical Statement Domain provides an abstract model used for the harmonization of all similar objects within V3.<br /> 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.” <br />
  63. 63. Alternatives to the RIM<br />OpenEHR<br />Semantic Web<br />SDMX<br />SNOWMED<br />- and the Web2 challenge<br />
  64. 64. Transparency and Best Practice<br />The availability of immediate audit and transparency has interesting implications for medical practice generally and for its practitioners. <br />Up-to-date audits could be made publicly available, and therefore help patients choose their surgeon. <br />Alternatively, audit information could be kept private, and only made accessible to the surgeon concerned or authorized auditors. <br />
  65. 65. Best Practice in Conducting Audits<br />Concepts of ‘best practice’ will vary with the way the delivery of cataract surgery is organized. <br />We the participants must drive the design and implementation of harmonious and efficient clinical and administrative processes. <br />
  66. 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.<br />
  67. 67. 6. Acknowledgements<br /><ul><li>We acknowledge the work of Mr. Derek Gower and Mr. Paul Symons of the NZ Software Corporation (‘Houston Medical’), and also the work of Mr. Jordan Schwab of Gravity Computing Limited. We also appreciate the contribution of many Ophthalmologists to the evolution of this approach, particularly Dr Kevin Taylor (New Zealand), Dr Sam Lertz (Australia), and Dr Brendan Vote (Australia).</li></li></ul><li>Thanks for listening<br />