This document discusses open source software projects at the Christie hospital, including their clinical portals and web forms. It provides details on the types of clinical data and workflows supported by the portals. Statistics on the number of forms in various stages of development and use are given. The document also discusses how the portals help integrate clinical outcomes data collection into normal workflows. Examples of improved cancer staging data collection and electronic nursing assessments are provided.

1. Dr Rhidian Bramley
Chief Clinical Information Officer
Director of Radiology
Using open source at
the Christie
CCIO Open Source Conference
Dr Jac Livsey
Clinical Outcomes Unit Lead
Consultant Clinical Oncologist
and how it supports agile software development
projects completed to date and current portal developments

2. Open Source at the Christie

4. Christie Clinical Portals

5. Christie Clinical Web Forms

7. Christie Clinical Web Forms
• Clinical noting
• Nursing & AHP assessments
• Clinical observations and results
• Care plans and actions
• Order communications
• Clinical referrals
• Discharge letters and TTOs
• Clinical outcomes
• MDT meetings
• Clinical audit
• Clinical research
• Staff & patient surveys

8. Christie Clinical Web Forms
Form Status Total
Concept 12
Design 73
Test QA 78
Live 162
Retired 23

9. Christie Clinical Web Forms
Structured clinical data
Real time clinical coding
Secure role based access
Record locking, audit and version control
Clinical workflow
Clinician and patient worklists
Simple and flexible design
Form and field templates
Build reusable questions and tables
Performance & analytics
Data quality reports and charting
Notifications
Messages and reminders
Set rules logic
Dynamic on data entry
Show/hide questions
Calculate risk
Customise care plans
Show alerts
Custom data model
Load and persist patient data
Clinical safety
Risk assessment scoring
Point of care decision support
Data display options
Tables, charts, journal, timeline
Easy and intuitive to use
Forms designed by clinicians
Logical relevant questions. Click and select

35. Medical care
• Provide patient care
• Record care given
And now…
• Collect clinical outcomes data

36. Dilemma
• Who has time to collect the data?
• Who is able to collect meaningful accurate
data?

37. Solution
• Integrate data collection so completely into the
workflow that it becomes part of the workflow not
extra to it.
• Making data collection replace the previous normal
record of patient care
• Designing intelligent, intuitive, clinically relevant
processes

38. Clinician completes disease specific web forms

41. Recording Clinical Tumour Stage
• Christie (2011) 41%
• Christie (2014) 90%

42. Overall survival by other factors

43. Electronic nursing
Previous nursing process
1. Admit patients, formulate care plans and complete
care plans on paper
2. Some data (CQUINS) entered electronically
3. Some data used for paper referrals
4. Some data collected by regular “walk arounds” and
spot checks

44. Electronic nursing
Problems
• Data collected up to 4 times
• Much of the data collection is distinct from patient
care
• Almost all the data recorded is not usable for analysis
• No real time data
• Very labour intensive
• Perceived as ‘box ticking’ waste of time by staff

54. Electronic nursing pilot
Feb 2014
49 nursing assessment forms created for pilot
• Forms were easy to use
• Saved time overall
• Increased time spent with patients
• Helped clinical decision making
• Improved data quality
• Allowed real time data capture

55. Electronic nursing pilot
Quotes
Nurse: “Fewer interruptions when completing with patients”
(compared to completing at a screen were people can ask you to
do other things and you get distracted).
Nurse: “We don’t want to go back to the old way”.
Nurse: “I want to give the patient as much information as
possible, now I can’t forget anything as I’m prompted on screen”.
Nurse: ”(We’ve) come back to the days where we know our
patient”.
Nurse: “There’s more patient involvement”.
Patient: ”The nurse told me so much more” (compared to a
previous appointment at the hospital, the patient felt that the
nurse told them more information and had more time to speak
to them).

56. MDT data capture

62. Pathway
MDT
Referral
• Patient and provisional diagnosis
• Clinical details for rad and path review
MDT
Meeting
• Confirm information
• Confirm diagnosis, staging and treatment
Clinic
• Review patient
• Agree management and treat

63. Conclusions
• Develop a system that allows the users to
directly interact and dictate design
• Work closely together to refine and improve
• Produce a tailored, user friendly, efficient
system that both improves patient care and
seamlessly collects the evidence to prove it