ISCaHN Treatment Dashboard: Providing Clinician Decision Support with Data Generated at the Point of Care

Graeme Bell and Chee Fon Chang

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 System (OIS) that is accessible and actionable for clinicians

This data can then be used to inform and support treatment decisions

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 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

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: – CINSW Enhanced Medical Oncology Reporting Project – Oncology Day Care Enhanced Scheduling Project– Activity Based Funding Extract– PROMPT care pilot project

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

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

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

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 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

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

Treatment Comparison

Carboplatin D1 Carboplatin D8

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

Results Progressive disease on treatment comparison is possible

Average patients delayed per cycle comparison is possible

Crombie et al ISCaHND1 Carbo n = 20 (%)

D8 Carbon = 20 (%)

D1 Carbo n = 83 (%)

D8 Carbo n = 97 (%)

7 (35) 8 (40) 33 (40) 38 (39%)

Crombie et al ISCaHND1 Carbo n = 20 (%)

D8 Carbon = 20 (%)

D1 Carbo n = 83 (%)

D8 Carbo n = 97 (%)

2 (10) 4.75 (24) 23 (27) 40.7 (42)

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

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

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

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

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. 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