Damian Fogarty is a Consultant Kidney Physician in the Regional Nephrology and Transplant Unit, Belfast Health and Social Care Trust. From 2010-14 was Chairman of the UK Renal Registry, an internationally recognised national audit body with many innovations and plaudits for its work. Damian has a particular interest in using routine data for quality improvement, better engagement with patient groups and the use of social media in all these areas. In this presentation at the Pathology Horizons 2015 conference of Cirdan,he discusses data analytics for pathology.
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Damian Fogarty on Pathology in the era of connected health: Linking patients, outcomes and data
1. PATHOLOGY IN THE ERA OF CONNECTED
HEALTH: LINKING PATIENTS, OUTCOMES
AND DATA
Damian G Fogarty
Consultant Nephrologist
Belfast Health and Social Care Trust
Former Chairman, United Kingdom Renal Registry
E: damian.fogarty@belfasttrust.hscni.net
@DamianFog
Pathology Horizons 5-7th November 2015
‘Better Data, Better Health’
3. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
3
4. DGF and patient Mary C age 72
Mary C’s GP has written:
“Dear Dr, We would appreciate your advice on this lady
with type 2 diabetes and an elevated serum creatinine.
Her creatinine is now 182umol/l (n <100). She has
angina, osteoporosis & SLE under steroid control”
No nephrotoxic medications
High Blood pressure 184/92
Proteinuria
Creatinine 190 umol/L
NEW MILLENNIUM
4
5. HER QUESTIONS & MY QUESTIONS
How will my symptoms be (if I have any)?
Will I have to travel to clinics a lot?
Will I live to see my Grandkids born/grow up?
Who will look after this…not my husband!
What do you think doctor?
What is her main disease? A renal biopsy?
What are risks for progression, hosp admissions,
CVS events, dialysis, survival?
What is the best form of dialysis for her?
How long will she live?
Which units manage them best?
5
7. Main ‘record’ with General Practitioner
EMIS, Vision etc
Patient administration in BCH
CSC managed PAS system…very old.
Lab records in BCH and RVH are linked
Notice that she is on the RVH diabetes system
Diabetes systems in most units in the UK
No systems for SLE, OP, IHD
DGF enters her on Renal eMED system
Renal systems in all units in the UK
MARY C DATA MANAGEMENT THEN
7
8. PROGRESSION OF CHRONIC KIDNEY DISEASE (CKD) TOWARDS
END STAGE RENAL DISEASE (ESRD)
8
From 2006 % kidney function now estimated with eGFR
9. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
9
13. INFORMATION NEEDS OF THE MAIN STAKEHOLDERS
Manage
PatientClinical
EvidenceDATA
Safe Effective
Efficient
13
14. 14
14
UK Renal Registry
reports on all kidney
replacement therapy patients
across the 4 nations
www.renalreg.com
Transplant
Haemodialysis (HD)
Peritoneal dialysis
(PD)
15. – Patient has left white blood cells at another hospital
– She has no rigors or shaking chills, but her husband states she was very
hot in bed last night
– The patient has been depressed since she began seeing me in 1993
– When she fainted, her eyes rolled around the room
– Between you and me, we ought to be able to get this lady pregnant
In early 1990s UK Renal Registry set up as an
electronic return of data registry
15
Paper records in healthcare
multiple, unstructured, disorganised, illegible, duplicate, physical
17. RENAL UNIT IT SYSTEMS
Several suppliers and systems (6 main ones)
Proton, Vital Data, eMED Renal, CCL, Cybernius, Renal Plus, & 4-6
others in single units
Marked variation in functionality and support
Direct feeds from PAS and lab systems +/- others
Common UKRR extraction system
Role of the ‘minimum’ data-set
400 items for the registry yet 9-10,000 items on the electronic patient
record/Renal IT system 17
18. 1991 Renal association initiated UKRR
1995 Standards and guidelines to define
what we will measure
1998 1st report
2015 18th report
71 adult and 13 paediatric units
UKRR TIMELINE
18
19. MAIN AIMS OF REGISTRIES
Enable clinicians to easily compile and review aggregate data
across units, regions, countries.
Activity
Incidence and prevalence data
Modalities of Renal Replacement Therapy
Markers of dialysis care
Biochemistry
Anaemia
Lipids and diabetes care (vascular risks)
Outcomes
Access to transplantation.
Infections
Mortality
Research questions
23. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
23
25. Growth in prevalent patients, by treatment modality
at the end of each year 1982-2010
0
10000
20000
30000
40000
50000
60000 1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
Year
Numberofpatients
PD
Home HD
HD
Transplant
25
26. 26
Figure 1.6. Median age of incident RRT patients by centre in 2012
White points indicate transplant centres
28. KIDNEY TRANSPLANTATION IN THE UK
DATE PUBLISHED: 8 APRIL 2014
0
1000
2000
3000
4000
5000
6000
7000
8000
2009/10 2010/11 2011/12 2012/13 2013/14
Waiting list
Live donor Tx
Deceased donor Tx
Total Kidney Tx
http://www.organdonation.nhs.uk/statistics/downloads/united_kingdom_mar14.pdf
28
32. UK Renal Registry 16th Annual Report
Figure 8.27. Serial 1 year survival for prevalent dialysis patients by UK country,
2000 to 2011 cohort years, adjusted to age 60
Note the confidence intervals…..no significant differences
32
35. Figure 8.17. The effect on survival after sequential adjustment for age,
Primary renal disease (PRD) and comorbidity, 2007–2011 incident cohort
35
36. Linkage to Hospital Episodes
Systems (England only)
21,633 Incident RRT Patients
2002 – 2006
UKRR Data until Oct 2009
2.8 Million Episodes
1996-2011
290,000 Hospital Admissions
(~13 per patient)
2 Million Outpatient Appt.
11,547 Deaths up to
31/12/2010
14.4% At Home
HES
Hospital Associated Mortality
Renal Centre & Hospital Level
Length of Stay & Freq. of Admission
Start of RRT & End of Life
Comprehensively Adjusted Survival
Late presentation, Comorbidity & PRD
Fotheringham et al
Nephrol Dial Transplant.
2014 Feb;29(2):422-30.
37. 0 200 400 600 800 1000 1200
0.50.60.70.80.91.0
Total Patients Per Centre
ProportionSurviving3Years
Mean Centre-Specific Survival at three years adjusted to age 65 and male: 69.7%, range 60.2 – 78.7%.
Six centres with worse than expected survival highlighted in red.
CENTRE SPECIFIC THREE YEAR SURVIVAL ON RRT
ADJUSTED FOR AGE AND SEX
Fotheringham et al
Nephrol Dial Transplant.
2014 Feb;29(2):422-30.
37
38. CENTRE SPECIFIC SURVIVAL ADJUSTED FOR AGE, SEX, ETHNICITY,
SOCIOECONOMIC STATUS AND 16 COMORBID CONDITIONS
0 200 400 600 800 1000 1200
0.50.60.70.80.91.0
Total Patients Per Centre
ProportionSurviving3Years
Mean Centre-Specific Survival at three years adjusted to all characteristics
including demography and comorbidity: 78.8%, range 72.9 – 86.3%.
1 centre with worse than expected survival highlighted in red.
95% comorbidity
coverage with
HES data
Fotheringham et al
Nephrol Dial Transplant.
2014 Feb;29(2):422-30.
38
39. NEED DEEPER & WIDER DATA TO REALLY ADJUST
Age
Gender
Ethnicity
Other disease (comorbidity)
Esp. diabetes, cancer
Late presentation for RRT
Social deprivation
Economic
Health service levels
39
40. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
40
42. INTEROPERABILITY CREATES OPPORTUNITIES FOR
NOVEL QUESTIONS
Bowel Cancer Screening
Existing & excellent NI Cancer Registry
Cancer Patient Pathway System (CaPPS)
NI Regional Accident and Emergency System (NIRAES)
Enhanced Prescribing Database (EPD)
? Prescriptions for constipation before cancer diagnosis
? Palliative care input via NIECR
? Death at home/hospice/hospital
42
43. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
43
44. RENAL PATIENT VIEW-~40,000 REGISTERED PATIENTS
44
• 1,500 logins/day
• 14,000 /month.
• Look at ~6.6
pages per visit
• Login ~4.34 mins
Successful pilot of patients entering their own blood pressure data
45. UKRR STUDY GROUPS & CHAIRMEN
Dialysis Study Group (Martin Wilkie)
Transplant Study Group (Iain McPhee)
Paediatric Research & Study Group (Manish Sinha)
Polycystic Kidney Disease Group (Tess Harris)
CKD Study Group (Nigel Brunskill)
Rare Disease Groups ( n=13 and growing...)
45
46. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
46
47. ISSUES
Data only as good as entered
Avoid duplication
IT investment alongside clinician and patient engagement
More needed on
Patient Reported Outcomes Measures PROMs
Patient Reported Experience Measures PREMs
Data governance important but too slow
Common Law/Data Protection Act in NI;
2006 & 2012 HSC bills in E&W
1997 Fiona Caldicott Principles
2013 Caldicott 2 added a 7th principle:
“duty to share information in the interests of the patients’ and
clients’ care”
47
50. RCTS FAR FROM PERFECT
Review of 74 nephrology RCTs
Four CONSORT indicators of randomization quality
26% provided no information at all on randomization.
Randomization type not reported in 40%
Method of allocation concealment not reported in 58%
Very expensive & slow
Data often not disclosed
Selection biases in both Cohorts/RCTs
Many clinical trials exclude high risk patients
Age, Cognition, Language, Social class, Compliance
Designed with many assumed apriori hypotheses
Steven Fishbane et al. Quality of reporting of randomization methodology in nephrology trials Kidney International (2012) 82, 1144–1146.
50
51. ANALYTICAL APPROACHES TO ACHIEVE QUASI-RANDOMISATION IN
RETROSPECTIVE DATABASE ANALYSES
Instrument variable
Inconsistent associations between pre-transplant dialysis modality and post-
transplant survival.
Use centre level % of PD as predictor variable in models.
No difference in outcomes between PD & HD patients
Propensity matching
Analysis of the Myocardial Ischaemia National Audit Project.
35,881 patients diagnosed with Non ST elevation ACS
eGFR of <60 ml/min: 15,680 (43.7%).
If eGFR 45-59ml/min 33% less likely to undergo angiography
If eGFR 15-29ml/min 64% less likely
YET 30% reduction in death at 1 year for those accessing angiography (adjusted
OR 0.66, 95%CI 0.57-0.77),
No evidence of modification by renal function/ CKD stage for this effect
(1) Kramer et al Nephrol. Dial. Transplant. (2012) 27 (12): 4473-4480
(2) Shaw C, Nitsch D, Junghans C, Shah S, O'Donoghue D, Fogarty D, Weston C, Sharpe CC. PLoS One. 2014
51
53. SUMMARY-DATA & ANALYTICS FOR PATHOLOGY
1. Individual
2. Intelligence (business)
3. Implementation
4. Interpretation-local knowledge
1. Interventions based on simple data
2. Interventions based on complex data
5. Interoperability & Integration
6. Involvement- Patients and patience
7. Issues-Information governance
8. In the future
53
54. VISION FOR 2020
100% of health & care encounters on integrated flexible
communicative IT systems
Involved patient in possession of e-notes
Better use of Health Informatics, Machine Learning
Better hardware & software
New methods for capture (phones, speech, video)
Faster database approaches (e.g. Hadoop, )
Better use of business intelligence in health
Analysts and Stats team in the hospital (Interpretation)
Analyses close to & with the patient (Interpretation)
Analysis by the patient of performance, safety etc
‘The Patient will see you now Doctor’ Eric Topol
54
55. WHAT HAPPENED TO MARY C?
EXPERIENCE IS OUR INTERNAL ‘REGISTRY’ 55
56. CONCLUSIONS
Health & social care data is our new oil
Mining it is only the first part
Understanding variation key
Improve quality
Reduces inefficiency & costs
Stimulates health service research questions
Detailed interpretation requires local knowledge
Patience required
Patient engagement esp. in IG & PREMs/PROMs
Registries, cohort studies and RCTs all needed as lots
of unknown unknowns left! 56
57. Special thanks to
UK Renal Registry
Ron Cullen
James Fotheringham
Charlie Tomson
Terry Feast
Peter Mathieson
Donal O’Donaghue
Centre for Public Health Queen’s University Belfast.
Collaborators: Students:
Aisling Courtney Elizabeth Reaney
Peter Maxwell Michael Quinn
Chris Cardwell Glynis Magee
Chris Patterson Sohel Badrul
Dermot O’Reilly Chris Hill
Frank Kee Andrea Rainey
Catriona Shaw
THANK YOU FOR YOUR ATTENTION