A PROMising Future Esther Kwong Academic F2 Dept Primary Care and Public Health The Relationship...
-
Upload
jenifer-wilford -
Category
Documents
-
view
213 -
download
0
Transcript of A PROMising Future Esther Kwong Academic F2 Dept Primary Care and Public Health The Relationship...
A ‘PROM’ising Future
Esther Kwong
Academic F2
Dept Primary Care and Public Health
The Relationship Between Patient Reported and Other Process
Outcomes at Trust Level
Project Supervisor: Dr Paul Aylin
Educational Supervisor: Dr Graham Easton
Dept Primary Care and Public Health
Contents
The Context –Setting the Scene for PROMs NHS
What are Outcomes
Measuring Patient Reported Outcomes
National PROMs Program Overview
Research Question
Methods
Results
Discussion & Conclusions
What have I learnt…
The Past
“Despite a century of developments in medical technology, and vast improvements in the ability of medical science to prevent, diagnose and treat disease and ill health, attempts to measure the outputs of health care in terms of their impact on patients’ health have not progressed beyond Florence Nightingale’s time.”
Getting the most out of PROMs Kings Fund 2010
The Context
Reasons for Healthcare:
Live Longer
Better Quality of Life
= Better Health Outcome
But health services traditionally focused on
one outcome
Mortality
Setting the Scene for PROMS
Darzi Review “High Quality for All”
NHS White Paper
Equity and Excellence: Liberating the NHS
Appraisal of new technologies –
PRO data incorporated in the evaluation of new technologies
Routine measurement of pre/post elective surgery PROMs
Since April 2009
DH Long Term Conditions PROMs Pilot
Since 2010
Patient Centred
If quality is to be at the heart of everything we do, it must be
understood from the perspective of patients.
Patients pay regard both to clinical outcomes and their experience of the service...
Lord Darzi The ultimate measure by which to judge the
quality of medical effort is whether it
helps patients, as they see it.
Donald Berwick
What Are Outcomes
Traditional Ways of Planning = Measuring in terms of OUTPUT
• Quantifying what is produced, implemented, provided, and developed in the health service
Increasing Focus = Measuring in terms of OUTCOME
• Quantifying extent of any health impact on patients
• Change in various dimensions ~physiological (e.g. functional status) or psychological (e.g. attitudes)
• Can be harnessed from different sources
Sources of Outcomes
Outcomes
Clinical
Clinician Reported
Patient Reported
Measuring Patient Reported Outcomes
Patient Reported Outcomes (PRO)• Health status as perceived by the patient
Patient Reported Outcomes Measures (PROMs)• Measurement tools to harness this information• Can be used in two points in time to record change in health status• Can be assessed against patient progress or health interventions
received• Various types available• Much dedicated research and analysis on validating questionnaire
types
Types of PROMS
EQ5D
For any condition
Different Disease states
Aggregation and comparison
Economic evaluations
Generic
Oxford Hip Score
Outperform on sensitivity
Centred on a particular aspect/ clinical detail
Focused – useful for informing
Condition Specific
National PROMs Program Overview
Since 1 April 2009
Providers required to collect and report PROMs
Four key NHS funded elective interventions• Unilateral hip replacements • Unilateral knee replacements• Groin hernia surgery• Varicose vein surgery
Expected to invite patients to complete a pre-operative PROMs questionnaire (Q1)
Post-operative questionnaires (Q2) are then sent to patients following their operation after a specified time period.
… the NHS will be the first health care system in the world to measure what it produces in terms of health, rather than in terms of the production of health care.Getting the most out of PROMs Kings Fund
… the NHS will be the first health care system in the world to measure what it produces in terms of health, rather than in terms of the production of health care.
Getting the most out of PROMs Kings Fund
PROMs Used for the National Program
• Multi-dimensional – five areas• Responses record three levels of severity• Scores are weighted and combined to give a
single index
EQ5D index score
• Self rating health related quality of life• Places self reported health state on a point in a
line• Line ranges from 0 to 100
EQ5D Visual Analogue Scale
• Validated tool specific for Total Hip Replacements• 12 questions to assess function and pain, 0-4
points• Given as a single summed score from 0 to 48
Oxford Hip Score
• Validated tool specific for Total Knee Replacements
• 12 questions to assess function and pain, 0-4 points
• Given as a single summed score from 0 to 48
Oxford Knee Score
Research Topic
Aim:
To explore the relationship between routinely collected patient reported and other process outcomes at trust level
Null Hypothesis:
There is no relationship between patient reported and other process outcomes at trust level
Methods:
Aggregate analysis conducted using STATA 11 on trust level data
Participation and Coverage 2010
Participation rate of 69.7%.
•245,488 eligible hospital episodes •171,080 pre-operative questionnaires returned
Return rate of 75.8%
•147, 974 post-operative questionnaires sent out• 112,163 returned
National PROMs Key Final Results 09-10 Overview
EQ-5D Index score87.2% of hip replacement respondents
77.6% of knee replacement respondents
Recorded an increase in general health following operation
Oxford Hip and Knee Score95.7% of hip replacement respondents
91.4% of knee replacement respondents
Recorded an improvement following operation
Data Sources
Hospital Episode Statistics (HES) PROMs data 2010 August 2011 Publication Used
Aggregate Trust Level Data
2010
Dr Foster Data
Orthopaedic Revision RatesOrthopaedic Readmission
CaseloadStaff to bed ratio
HSMR
National Joint Registry Data
Orthopaedic Procedures Caseload
HES Inpatient Data
Elective Surgery Waiting TimesEmergency Admission Caseload
Data Comparison
PROMs Outcomes 2010 Data- Hip and Knee Data
Case Adjusted Health Gain
(Q2-Q1)
• EQ5D Index • EQ5D Visual Analogue Scale• Oxford Hip/ Knee Score
Other Process Outcomes/ Hospital Indicators Compared
1. Hospital Standardised Mortality Ratios
2. Dr Foster Orthopaedic Revision Relative Rate
3. Dr Foster Orthopaedic Readmission Caseload
4. National Joint Registry Data Orthopaedic Procedure Caseload
5. Hospital Episode Statistics Elective Surgery Waiting times
6. Hospital Episode Statistics Emergency Admissions Caseload
7. Hospital Staff to Bed Ratios
Descriptive Results Hip
Outcome Case
Numbers
Number of Trusts
(Observations)Missing Trusts
MeanStandard Deviation
Inter-quartile Range
EQ5D Index 22,270 127 21 0.395 0.0364 0.4215
EQ5D VAS 21,653 128 20 7.53 2.23 3.362
Oxford Hip Score
24,682 131 17 19.3 1.28 20.1
05
10
15
Den
sity
.25 .3 .35 .4 .45Case Adjusted EQ5d HG
0.0
5.1
.15
.2D
en
sity
0 5 10 15EQ5D VAS Case Adjusted health gain
0.1
.2.3
.4D
en
sity
14 16 18 20 22Oxford Hip Score Case Adjusted health gain
Correlations Hip
Indicator EQ5DIndex
EQ5DVAS
OHS
Revision Relative Risk -0.05 0.0308 -0.1263Readmission
Caseload -0.0919 0.0847 -0.1431
SurgeryWait Times
(days) -0.0275 0.0777 0.0606Emergencies
Caseload -0.0225 -0.0143 -0.137Hip Procedures
Caseload 0.2042 0.1939 0.1567 Orthopaedic Procedures Caseload 0.1924 0.1881 0.1258
Nurse to Bed Ratio -0.0295 -0.0645 -0.066
Staff to bed Ratio -0.0402 -0.0801 -0.0437
HSMR 0.0573 0.0451 0.0334.2
5.3
.35
.4.4
5E
Q5D
Inde
x C
ase
Adj
uste
d H
ealth
Gai
n
0 500 1000 1500NJR no. Hip operation procedures
Case Adjusted EQ5d HG Fitted values
05
1015
EQ
5D V
AS
Cas
e A
djus
ted
Hea
lth G
ain
0 500 1000 1500NJR no. Hip operation procedures
EQ5D VAS Case Adjusted health gain Fitted values
1416
1820
22O
HS
Cas
e A
djus
ted
Hea
lth G
ain
0 500 1000 1500NJR no. Hip operation procedures
Oxford Hip Score Case Adjusted health gain Fitted values
Regression Hip Regression Model
Hip F Probability b Coefficient R2
ValueConfidence
Intervals
EQ5D index Hip Operation
Caseload0.0398 0.0000276 0.0334
1.31 x 10-5
539 x 10-5
EQ5D VASHip Operation
Caseload 0.0612 0.0015296 0.0275
-7.27 x 10-5
313 x 10-5
OHSHip Operation
Caseload0.0460 0.0009363
0.0305 1.69 X 10-5
186 x 10-5
EQ5D index Orthopaedic
Operation Caseload0.0453 0 .0000138 0.0317
0.0291 x 10-5
2.72 x 10-5
EQ5D VASOrthopaedic
Operation Caseload0.0640 0.000775 0.027
4.58 x 10-5
159 x 10-5
OHS OrthopaedicOperation Caseload 0.0742 0.000425 0.0245
4.33 x 10-5
90.2 x 10-5
EQ5D VASWaiting Time
0.0836 0.0378 0.0267-510 x 10-5
8030 x 10-5
OHSWaiting Time
0.0734 0.0223 0.0281-210 x 10-5
4670 x 10-5
Description Results Knee
Outcome Case Numbers
Number of Trusts
(Observations)
Missing Trusts
Mean Standard Deviation
Inter-quartile Range
EQ5D Index23,318 180 219 0.299 0.0369 0.45
EQ5D VAS 22,591 177 222 1.836 2.105 3.035
Oxford Knee Score 25,413 189 210 14.81 1.43 1.653
05
10
Den
sity
.2 .25 .3 .35 .4 .45 EQ5D case adjusted HG
0.0
5.1
.15
.2D
en
sity
-5 0 5 10EQ5D VAS Case Adjusted HG
0.1
.2.3
Den
sity
10 12 14 16 18 20Oxford Knee Score case adjusted HG
Correlations Knee
Indicator EQ5DIndex
EQ5DVAS
OKS
Revision Relative Risk
-0.09 -0.0795 -0.1459
Readmission Caseload
0.1524 0.0124 0.2188Surgery
Wait Times (days)
-0.0408 0.0388 0.1257
Emergencies Caseload -0.0447 -0.1586 -0.0283
Hip Procedures Caseload
0.2176 0.1014 0.181 Orthopaedic Procedures Caseload
0.2215 0.0389 0.1693
Nurse to Bed Ratio
-0.2407 -0.1899 -0.3408
Staff to bed Ratio-0.3242 -0.4102 -0.185
HSMR0.102 0.104 0.0625
.2.2
5.3
.35
.4.4
5E
Q5D
Inde
x C
ase
Adj
uste
d H
ealth
Gai
n
9 9.5 10 10.5 11 11.5HES Emergency Admissions Caseload
EQ5D case adjusted HG Fitted values
-50
510
EQ
5D V
AS
Cas
e A
djus
ted
Hea
lth G
ain
9 9.5 10 10.5 11 11.5HES Emergency Admissions Caseload
EQ5D VAS Case Adjusted HG Fitted values
10
12
14
16
18
20
OK
S C
ase
Ad
just
ed
He
alth
Gai
n
50 100 150 200 250Foster RR Knee Revision
Oxford Knee Score case adjusted HG Fitted values
Regression Knee
Regression model F Probability b Coefficient R2
ValueConfidence
Intervals
EQ5D index Emergency Admissions
Caseload0.0003 -0.0158 0.135 -0.024
-0.007
EQ5D VASEmergency Admissions
Caseload0.0341 -0.488 0.0372 -0.94
-0.037
OKSEmergency Admissions
Caseload0.0331 -0.350 0.0382 -0.77
-0.033
Discussion – Important Findings 1
Weak positive correlations between Hip and Orthopaedic Procedures Caseload and all Hip PROMs health gain • Suggests the more procedures a trust does the better its quality
of hip replacement procedure perceived by patient• This is an expected correlation direction
Weak negative correlations between Emergency Admission Caseload and all Knee PROMs health gain • Suggests the more emergency admissions a trust has the worse
the patient perceived outcome for a knee replacement procedure• unexpected correlation direction, warrants further exploration into
relational factors – such as trust specialisation and quality relationship
Discussion -Important Findings 2
Weak Positive Correlation Between EQ5D Visual Analogue Scale and Oxford Hip Score health gain for hip patients and Waiting Times for Elective Surgery
• Suggests the longer a patient waits for elective surgery in a trust the more health gain perceived from hip operation
• Unexpected correlation direction • Disease progression factors are adjusted for• May be explained by expectation management
‘Patient Satisfaction = Patient Experience - Patient Expectation’• Longer waiting times may decrease expectation affecting
perceived outcome • Lead time difference
Discussions Limitations
Recruitment Bias• LSHTM Report to Dept of Health on PROMs recorded correlation
coefficient of -0.38 between EQ5D score and every 20% increased recruitment, suggesting low recruitment rates can introduce bias
• The report recommended a target recruitment rate of 80%
Response Bias• Studies suggest non responders were younger in all PROMs, This is
particularly evidenced in orthopaedic PROMs
Patient Reported Outcome Measures (PROMs) in Elective Surgery Report to the Department of Health, London School of Hygiene and Tropical Medicine
Conclusions
Weak/ Lack of Correlations suggests Patient Reported Outcomes are capturing an added dimension of quality that traditional process outcome and clinical indicators were not measuring
Weak correlations findings at trust level maybe due to aggregation, this could eliminate clinical variation within and between hospital services as well as patient characteristics• Evidence from clinical governance concluded acute hospitals services were
‘A mix of good and bad’• Analysis of PROMs at clinical level and unadjusted data may provide further
explanations and strengthen correlations
Lack of evidence/ data available for statistical relationship significance for correlations • Further work building larger aggregate data set on PROMs• Analysis on new PROMs data, or analysis spanning two years of PROMs data
What I learnt from this Academic Rotation
Nature of Rotation
2 days a week in GP surgery clinical duties
3 days a week dept based research and teaching activities
Research
Literature search on PROMs
Insight in health services research
Handling aggregate data
Data management
Statistical analysis on STATA
Seminars and Journal Club
Experience of life as an academic!
TeachingFormal Teaching courses/ training
Clinical Methods teaching 3rd year Imperial Students
Problem Base Learning facilitator
GPWealth of clinical experiences
Primary care setting exposure
Consultation simulation training
References
Bevan G , Skeller M, 2011 Competition between hospital and clinical quality BMJ 2011; 342:d3589Berwick D, Hiatt H, Janeway P, Smith R. 1997 An ethical code for everybody in health care BMJ 1997;315:1633Black N, Browne J, Cairns J. 2006. Health care productivity. British Medical Journal 333: 312–313.Brooks R, Rabin R, de Charro F. 2003. The Measurement and Valuation of Health Status using EQ-5D: A European Perspective. Kluwer: Dordrecht.Browne J, Jamieson L, Lawsey, J, van der Meulen J, Black N, Cairns J, Lamping D, Smith, S, Copley L,Horrockes, J. 2007. Patient Reported Outcome Measures (PROMs) in Elective Surgery. Report to theDepartment of Health. Available from: www.lshtm.ac.uk/hsru/research/PROMs-Report-12-Dec-07.pdf.Burge P, Devlin N, Appleby J, Gallo F, Nason E, Ling T. 2006. Understanding patients’ choices at the point of referral. Technical report TR359-DOH, Cambridge: RAND Europe. Available from: www.rand.org/pubs/technical_reports/TR359/.Darzi L. 2008. High Quality Care for All. NHS Next Stage Review: Final Report, Department of Health, London.Available from: www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/DH_085825.Dawson J, Rogers K, Doll H, Using Patient Reported Outcomes Routinely: An example in context of Shoulder surgery Open Epidemiology Journal 2010, 3,42-52 Department of Health. 2008. Guidance of the Routine Collection of Patient Reported Outcome Measures (PROMs).Department of Health document DH_081179[1].pdf.Devlin N, Appleby J. 2010. Getting the Most Out of PROMs: Putting Health Outcomes at the Heart of NHS Decision Making. Kings Fund/Office of Health Economics: London.Dolan P. 1997. Modelling valuations for EuroQol health states. Medical Care 35(11): 1095–1108.2010).Greenhalgh J, Long A, Flynn Rob, 2004 The use of patient reported outcome measures in routine clinical practice: Lack of theory or lack or impact. Social Science and Medicine 60 (2005) 833-843EuroQol Foundation. Springer: Rotterdam.Hospital Episode Statistics: Finalised Patient Reported Outcome Measures (PROMs) in England: April 2009 – March 2010 Isis Outcomes Patient Reported Outcome Measures from the University of Oxford, Orthopaedic Pros http://www.isis-innovation.com/outcomes/orthopaedic /
London School of Hygiene and Tropical Medicine Patient Reported Outcomes on Elective Surgery, Report To Department of Health Dec 2007, http://www.lshtm.ac.uk/php/hsrp/research/proms_report_12_dec_07.pdf NHS North West. 2010. Advancing quality. Available from: www.advancingqualitynw.nhs.ukNational Council on Ageing and Older People, 1998 health promotion strategy for older people in Ireland. Adding years to life and life to yearsNational Network of Libraries of Medicine Guide 3: Define Measurable Goals, Outputs and Outcomes http://nnlm.gov/outreach/community/goals.htmlOffice of Health Economics. 2008. NHS Outcomes, Performance and Productivity. Report of the Office of Health Economics Commission. OHE: London.Szende A, Oppe M, Devlin N. 2007. EQ-5D Valuation Sets: An Inventory, Comparative Review and Users’ Guide.
Acknowledgements
I want to thank all those in the department who have contributed their expertise and advice towards this project and towards my educational development
• Dr Paul Aylin, Dr Graham Easton, Dr Jenny Lebus, • Dr Michael Soljak, Dr Sonia Saxena• Dr Fiona Hamilton, Dr Matthew Harris, Dr Eszter Vamos• Elizabeth Cecil, Farzan Rahman, Dr Ghasem Yadegarfar