On-going development and new thinking is critical to ensuring a deeper understanding of the patient’s experience and reported outcomes through both innovative approaches to data interpretation and dissemination in providing real decision choices in the delivery of improved care.
In a previous blog I wrote about the importance of story-telling in the understanding of the patient’s experience in the context of Discovery Interviews. Here I would like to focus on how bringing information from additional sources and combining this with the patient’s story can facilitate our understanding of the patient’s experience and outcomes.
This new methodological thinking a ‘holistic’ analysis combines both hard research data such as survey reports, frequencies of complaints etc. with the more ‘softer’ qualitative type such as the patient’s own story combined with the health professional’s insight and intuition.
This combination of hard and soft data with the health professional’s insight and intuition, nevertheless needs to be with an analytical framework of rigorous evaluation. The recommended steps within this framework include:
- Ensuring we have a full understanding of the big picture
- Compensating for less than perfect data
- Evaluating our sources of information in terms of their validity, weight, power and direction
- Integration of the research evidence
- Reframing the data to provide new insights, for example by the use of narrative – that is the aggregated findings of the integrated evidence is presented as a single patient/carer.
Expanding on two elements from above to illustrate the point, let’s look at evaluating the ‘direction of evidence’ as part of the overall process of data integration. Here we need to look for both internal and external consistency within our various data sets.
- Internal consistency: is there high or low-level of internal consistency within each of our data sets in terms of the overall evidence of the subject under study? Is it consistently weak or strong in each of the data sets?
- External consistency: Here we are looking for consistency across the various data sets. Are we finding that all the available evidence is pointing in much the same direction? Is there consistency for example, in the issues raised in our qualitative research e.g. is our Discovery Interviews, consistent with the evidence that is coming from survey findings, clinical audit and complaints? Does this match the prior perceptions of the care staff and management?
Let’s take another example where we want to look at the factors behind patient treatment adherence to a new therapy intervention. Our current data sets include;
- scores on a patient reported outcome (PRO) measure
- Symptom score
- Anecdotal evidence from clinicians
- In-depth interviews with both adherent and non-adherent patient groups
Integrating the different data sets into the overall analysis
Weight of evidence
In terms of the overall balance of evidence we see that the in-depth interviews, PRO data, symptom scores and clinical opinion we can paint a fairly favourable or unfavourable picture of the new therapy.
Power of evidence
Here we are looking for the degree of convergence between each of various external evidence with the opinions of specialists in the field.
We need also to take account of the robustness of the various evidence bases. How was this evidence collected? Were there inbuilt biases in the methodology?
We should also look at the quality of the anecdotal evidence from clinicians and the tendency for bias reporting
Direction of evidence
The important issue here the consistency of the evidence in each of the evidence bases. Were there variations for example in age, sex, disease duration across the different sets of evidence? How consistent were the findings across the various sources of evidence, PRO scores, symptoms, in-depth interviews and clinical report?
Communicating the research findings as a narrative
Having applied the holistic approach to our data analysis in making sense of the various data sources, there is now the need to present a plausible and coherent presentation that can account for the identified variations and patterns in the data.
At DHP Research we are keen and active exponents of the narrative in explaining the complexities of research data. By providing a coherent narrative we believe it’s more likely to be absorbed and understood as well as recalled and as a result will more likely support the decision-making process. Narrative is also particularly effective in linking together the key relationships and provide a vivid picture of the study findings.
Our approach with maximising the information from Discovery Interviews for example will involve all the stages of analysis described above but linking that with input from our front line clients to derive patient/carer archetypal groups from which the study finding can be communicated as a narrative. A similar approach can be applied to our non-adherence example.
An example of how we apply the narrative in communicating study findings
The patient undergoing this therapy will most likely continue to non-adhere to the treatment regimen
- … as long as it continues to have contraindications ..vomiting (%) and headaches (%)
- However, the patient is concerned of the long-term effects of non-adherence (%) but, is also aware that there are other similar products on the market (%).
- Given the contraindications and lack of perceived benefits on trial, the patient will likely revert to their original treatment (%).
Clearly there is the need for continuous checks and balances to be applied and monitored. Nevertheless this approach is in sharp contrast to the traditional building block of presenting research findings where information is presented in linear form from introduction, methods, results and discussion.
The narrative approach identifies the key factors and patterns in the data drawing on supporting evidence as the story unfolds.
The narrative approach helps close the gap between the data and decision-making process and helps engage the audience and facilitate a more effective decision making process.
If you want to know more about our approach to the analysis of patient experience data contact us: firstname.lastname@example.org
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Categories: Patient reported outcomes