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ISCaHN Treatment Dashboard:
Providing Clinician Decision Support with
Data Generated at the Point of Care
Graeme Bell and ...
Aim
 To describe the development of a treatment dashboard at
Illawarra Shoalhaven Cancer and Haematology Network
(ISCaHN)
Treatment Dashboard
 The aim of the dashboard is to present data extracted in
real time from our Oncology Information Sys...
Outline
 Foundation
 Development
 Production
Foundation
 Rapid Learning System (RLS)
 Oncology Information System
Foundation - RLS
 Etheredge defined a rapid learning health care model as
one that generates as rapidly as possible the e...
RLS
Challenges for Big Data and RLS
 Data correctness
 Data completeness
 Data consistency
 Data storage
Development
 Dashboard not developed in isolation
 Result of experience from multiple extraction projects
including:
– C...
Development
 From lessons learnt in extract projects we were able to
develop an extract with relevant clinical data
 Thi...
Intake Data
 You can only pull out what you've put in
 Ensure quality and completeness of data
– Use of manual and autom...
Data Transformation and Aggregation
 Treatment dashboard
 Chemotherapy protocols – different protocols dependent
upon di...
Production - Treatment Dashboard – Care
Plan Selection
Treatment Dashboard – Ipilimumab
Treatment Dashboard - Ipilimumab
Treatment Dashboard – Toxicities and
Demographics
Treatment Dashboard Filter
Carboplatin/Gemcitabine in NSCLC
D1 or D8 Carboplatin?
 Carboplatin combined with Gemcitabine has an established
role in ...
Carboplatin/Gemcitabine in NSCLC
D1 or D8 Carboplatin?
 Based on the results of the Crombie study, eviQ
superseded their ...
Treatment Comparison
Demographics
Carbo D1 Carbo D8
Carbo
D1
Carbo
D8
Sex Male % 70 45
Sex Female % 30 55
Stage III % 15 30
Stage IV % 85 70
Cr...
Results
 Progressive disease on treatment comparison is possible
 Average patients delayed per cycle comparison is possi...
Results
 Toxicity comparison will be possible, still working on bugs
in the report and display of these
 Unable to provi...
Dashboard Difficulties
 Similar to large scale difficulties
– Incomplete data entry
– Inconsistent data entry
– Incorrect...
Conclusion
 Able to extract, aggregate and analyse data generated at
point of care to inform and optimise patient care
 ...
Conclusion
 There are holes in the data – requires continued audit and
QA
 Engage with staff, make the data presentable ...
Thanks
 Chee Fon Chang
 Anthony Arnold
 Amy Hains
References
Abernethy AP, Etheredge LM, Ganz PA et al. Rapid-Learning System for Cancer Care.
Journal of Clinical Oncology....
ISCaHN Treatment Dashboard: Providing clinician decision support with data generated at the point of care
ISCaHN Treatment Dashboard: Providing clinician decision support with data generated at the point of care
ISCaHN Treatment Dashboard: Providing clinician decision support with data generated at the point of care
ISCaHN Treatment Dashboard: Providing clinician decision support with data generated at the point of care
ISCaHN Treatment Dashboard: Providing clinician decision support with data generated at the point of care
ISCaHN Treatment Dashboard: Providing clinician decision support with data generated at the point of care
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ISCaHN Treatment Dashboard: Providing clinician decision support with data generated at the point of care

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Illawarra Shoalhaven Cancer and Haematology Network (ISCaHN) has been using an oncology information system (OIS) as a complete electronic record for over 4 years. There has been both considerable and valuable treatment data generated at the point of care. Are we able to rapidly assess the outcomes of our own treatment data, and use this outcome data to help inform the delivery of care to our patients?

Veröffentlicht in: Gesundheit & Medizin
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ISCaHN Treatment Dashboard: Providing clinician decision support with data generated at the point of care

  1. 1. ISCaHN Treatment Dashboard: Providing Clinician Decision Support with Data Generated at the Point of Care Graeme Bell and Chee Fon Chang
  2. 2. Aim  To describe the development of a treatment dashboard at Illawarra Shoalhaven Cancer and Haematology Network (ISCaHN)
  3. 3. Treatment Dashboard  The aim of the dashboard is to present data extracted in real time from our Oncology Information System (OIS) that is accessible and actionable for clinicians  This data can then be used to inform and support treatment decisions
  4. 4. Outline  Foundation  Development  Production
  5. 5. Foundation  Rapid Learning System (RLS)  Oncology Information System
  6. 6. Foundation - RLS  Etheredge defined a rapid learning health care model as one that generates as rapidly as possible the evidence needed to deliver quality patient care 1  Users learn as much as possible as soon as possible through the collection of data at the point of care that can then be used to inform clinical care and service delivery  Whilst this model has been developed around the concept of “big data”, it is also possible to apply it at a localised level to achieve similar outcomes 1. Abernethy et al, 2010
  7. 7. RLS
  8. 8. Challenges for Big Data and RLS  Data correctness  Data completeness  Data consistency  Data storage
  9. 9. Development  Dashboard not developed in isolation  Result of experience from multiple extraction projects including: – CINSW Enhanced Medical Oncology Reporting Project – Oncology Day Care Enhanced Scheduling Project – Activity Based Funding Extract – PROMPT care pilot project
  10. 10. Development  From lessons learnt in extract projects we were able to develop an extract with relevant clinical data  This data is then displayed in a dashboard for clinicians to access in a readable and accessible form
  11. 11. Intake Data  You can only pull out what you've put in  Ensure quality and completeness of data – Use of manual and automated QA's – Regular staff training, education and support  Data needs to be accessible and actionable for clinicians
  12. 12. Data Transformation and Aggregation  Treatment dashboard  Chemotherapy protocols – different protocols dependent upon diagnosis, stage, co-morbidities  Gold standards in curative disease, greater variability in palliative setting  Dash board not solely a tool for clinicians, we aim to develop an option for patient viewing, so that they can be walked through treatment options, empowering them in their own treatment decision
  13. 13. Production - Treatment Dashboard – Care Plan Selection
  14. 14. Treatment Dashboard – Ipilimumab
  15. 15. Treatment Dashboard - Ipilimumab
  16. 16. Treatment Dashboard – Toxicities and Demographics
  17. 17. Treatment Dashboard Filter
  18. 18. Carboplatin/Gemcitabine in NSCLC D1 or D8 Carboplatin?  Carboplatin combined with Gemcitabine has an established role in the treatment of advanced NSCLC  In 2009 Crombie et al evaluated Two 21 day gemcitabine- carboplatin schedules  Phase II study where 40 patients were given Gemcitabine on D1 and Day 8 of a 21 day cycle, with patients being randomized to having Carboplatin on either D1 or D8 of their treatment  Results of the study showed that Carboplatin administered on D8 resulted in lower dose intensity and more dose delays
  19. 19. Carboplatin/Gemcitabine in NSCLC D1 or D8 Carboplatin?  Based on the results of the Crombie study, eviQ superseded their Carbo/Gem (D8 Carbo) protocol in July 2013, and left only the Carbo/Gem (D1 Carbo) protocol approved  At ISCaHN, we had also made a similar change in practice  From January 2011 to March 2013 patients were prescribed the Carboplatin/Gemcitabine protocol with D8 Carboplatin  From March 2013 the majority of patients were prescribed Carboplatin/Gemcitabine, with carboplatin being delivered on D1
  20. 20. Treatment Comparison
  21. 21. Demographics Carbo D1 Carbo D8 Carbo D1 Carbo D8 Sex Male % 70 45 Sex Female % 30 55 Stage III % 15 30 Stage IV % 85 70 Crombie et al
  22. 22. Results  Progressive disease on treatment comparison is possible  Average patients delayed per cycle comparison is possible Crombie et al ISCaHN D1 Carbo n = 20 (%) D8 Carbo n = 20 (%) D1 Carbo n = 83 (%) D8 Carbo n = 97 (%) 7 (35) 8 (40) 33 (40) 38 (39%) Crombie et al ISCaHN D1 Carbo n = 20 (%) D8 Carbo n = 20 (%) D1 Carbo n = 83 (%) D8 Carbo n = 97 (%) 2 (10) 4.75 (24) 23 (27) 40.7 (42)
  23. 23. Results  Toxicity comparison will be possible, still working on bugs in the report and display of these  Unable to provide comparison for response rates as this is currently poorly and/or not uniformly documented in day to day clinical practice  Survival rates/time currently not calculated, but will be possible in future
  24. 24. Dashboard Difficulties  Similar to large scale difficulties – Incomplete data entry – Inconsistent data entry – Incorrect data entry  Survival outcomes, particularly for positive prognostic early stage dx (breast, colon etc), requires lengthy time for measurement of PFS rates and OS rates
  25. 25. Conclusion  Able to extract, aggregate and analyse data generated at point of care to inform and optimise patient care  Ability to identify and measure patterns and trends in real time  Visualisation of data enables rapid hypothesis generation  Possible to quickly compare treatment data with that from published clinical trials
  26. 26. Conclusion  There are holes in the data – requires continued audit and QA  Engage with staff, make the data presentable and actionable, giving a reason for complete and correct data entry
  27. 27. Thanks  Chee Fon Chang  Anthony Arnold  Amy Hains
  28. 28. References Abernethy AP, Etheredge LM, Ganz PA et al. Rapid-Learning System for Cancer Care. Journal of Clinical Oncology. 2010;28(27): 4268-4274 Crombie C, Burns WI, Karapetis C, Lowenthal RM et al. Randomized phase II trial of gemcitabine and either day 1 or day 8 carboplatin for advanced non-small-cell lung cancer: Is thrombocytopenia predictable? Asia-Pacific Journal of Clinical Oncology 2009;5: 24-31

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