Using EQ-5D to measure patient reported outcomes

Transcription

1 Using EQ-5D to measure patient reported outcomes April 2017 Liz Vernon-Wilson Outcomes Researcher Tom Rudd Unit, Moorgreen Hospital 1

2 Introducing PROM data EQ-5D-5L is a Patient Reported Outcome Measure or PROM. A short questionnaire that the service user completes to describe their health and wellbeing. Collecting PROM data at multiple time points enables an individual s progress to be followed. PROM responses can inform care plan goals. Contextualised aggregated data from many similar service users can help us understand how well our services perform from our service users perspective. 2

3 About the EQ-5D PROM The EQ-5D-5L questionnaire has two parts. First, 5 domain questions ask about specific issues: mobility self-care usual activities pain or discomfort anxiety or depression. The EQ-5D-5L uses 5 levels of responsiveness to measure problems. The range is; no problem - disabling/extreme The domain items can analysed individually or be conflated into one Index score. 3

4 The EQ-5D VAS The second part of EQ-5D data is a global score. This gives an overall impression of wellbeing today. Service users rate from 0 (worst health imaginable) to 100 (best health) on a visual analogue scale (VAS). Collecting other information helps contextualise the outcomes data. We can use Point of care (admission/discharge) Intervention Duration Diagnosis, Care cluster (mental health) or other health resource group (HRG) 4

5 Working with PROs Contextualised PROM responses can be used to compare interventions or teams. They are a starting point for reflective practice; discussions about what works well and why, help us improve. It can also be helpful to put PRO (patient reported outcome) information alongside clinician rated measures, or other quality or process indicators. Different patient groups could start with quite di ssimilar PROM profiles and achieve different outcomes over time. 5

6 Contextualising OPMH outcomes. Mental health care clusters are a needs-based classification system. Allocation to a particular care clusters depends on broad identification of the nature of someone s presenting problems and current severity of their symptoms. Clusters fall into three super groups; non-psychotic, psychotic or organic, and range from low needs to severe. Cluster choice acknowledges risk, chronicity and likely duration of care episode too. Service users in different clusters can start with different EQ-5D profiles and achieve different outcomes. 6

7 Distinguishing Service User Groups (VAS) New referrals to OPMH can usually be distinguished by their EQ-5D-5L scores. People entering memory services (cluster 18) often give very positive responses to the health thermometer question. They typically describe few problems on the domain questions. This compares quite differently to people referred for functional MH problems (cluster 3 & 4). The graph demonstrates the distribution of VAS/thermometer scores collected for these two groups at referral. High scores indicated good levels of self-rated wellbeing. Index scores also follow a similar pattern. Cluster 18, n=1011; cluster 3&4 n=330 7

8 Distinguishing Service User Groups (Index) The index score represents a summary of domain questions. Service users who respond No problem to all five questions return a domain score of 1, whilst someone responding with the most extreme/disabling problem on all five questions returns an index score of The graph shows people with memory problems (cluster 18) return higher index scores (fewer problems) than people with functional mental health problems (cluster 3 & 4). Possible range Observed range Cluster 18 n=992; cluster 3&4 n=301 8

9 Expected Outcomes Before examining outcomes data, it can be helpful to state what changes in the data are expected. For example, is it reasonable to anticipate older people who have good wellbeing scores will remain well, or deteriorate over time? If people record a low score, what change is it reasonable to expect and by when? Would this be in the holistic score or domain score? By review or by discharge? Now you can test the data and your assumptions. 9

10 Different Outcomes revealed Paired data reveals different patterns for the two groups of service users with different presentations described. Cluster 18 pairs lie around the No change in score line, whilst the points from service users in clusters 3 & 4 are more likely to fall above the line (top left shows improvement). There is statistical difference in the before and after treatment scores for service users in the cluster 3 & 4 group, but not for those in cluster 18. This comparison principle could be applied to treatments, wards etc. 10

11 What clinical changes occur? Examining change in each of the 5 domains shows that the greatest difference before and after treatment is observed in the A nxiety/depression domain score. Change is also noted in domain 3, Usual activities, demonstating how mental health impacts on everyday functioning. Both of these domains demonstrate statistically significant differences before and after treatment (p< 0.01). n=36 11

12 Case Study; Albert Case studies help us understand how particular interventions, interactions and relationships support recovery. This service user s name has been changed. Albert s wife of 50 years died in 2015, following a short illness. He has suffered with a severe grief reaction. Albert has experienced lasting depression and anxiety since his bereavement. Initially, his symptoms included diurnal variation in mood, sleep disturbance, severe loss of appetite, psychomotor agitation, guilt & self-blame. Albert also developed marked anxiety including cario-respiratory features of panic. He has a history of depression. His interventions have included; Anti-depressants 4 weeks admitted care Bereavement counselling, music therapy OT support Crisis plan for times of distress WRAP Coping strategies PTS 12

13 Albert s PROM scores help track his progress. They demonstrate the impact of interventions and transitions in care. Admission CMHT support reduced after discharge 1st Anniversary of wife s death Choosing to disengage from psychological therapy Discussing these scores and the case history highlighted transitions between care teams and professionals need careful management. What would your team do with this information?

14 Summary Patient reported outcome measures, such as EQ-5D-5L, demonstrate our service users view on changing health and wellbeing states as they travel care pathways. Contextualising PROM data with information about team, diagnosis, intervention and duration of care episode offers insight into clinical effectiveness. Here, EQ-5D data has distinguished different patterns of change in health state for older aged adults accessing integrated community mental health services. Those referred with early cognitive impairment reported stability in global wellbeing. Service users referred for non-psychotic mental health problems including anxiety & depression reported improvement in this domain, their usual activities and global wellbeing. Comparison of PROM data with other information from the EPR, such as clinician reported outcome and detail about intervention will enhance our potential to use outcomes data effectively. 14

15 For more information on your team s outcomes data, please contact: Liz Vernon-Wilson at Elizabeth.vernon-wilson@southernhealth.nhs.uk Clinical Outcomes Tom Rudd Unit, Moorgreen Hospital Southampton SO30 3JB