Patient generated data, electronic health records, clinical outcomes data, Hospital Episodes Statistics – just some examples of the vast amounts of quantitative data, or Big Data, that can be extracted and used to inform healthcare decision making.
But what about qualitative data?
For example, we have access to information about how diabetic patients are responding to measures to reduce their blood sugar levels. There is the HbA1c measurement, values pertaining to the functioning of their kidneys, blood pressure readings, weight checks etc. Would it be useful to know how patients engage with the measures outlined for them? Would it then also be useful to know why patients might struggle with some measures? People’s attitudes, perspectives, feelings and indeed behaviour are highly subjective, complex, and therefore in the main, immeasurable – but they are also, arguably, an important part of the patient journey.
How can unquantifiable data be identified
Big Data (quantified) is likely to be the starting point. This provides the mechanical or digital statistics that enables analysts to identify the big questions. Big Data establishes what is happening. In my opinion, it becomes far more meaningful with qualitative data establishing why for a more complete story.
Quantitative research is objective, establishing a wide range of data for statistical analysis where all other criteria are equal. A simple example is the study of comparative blood pressure levels of a large sample group after resting or following physical stimulation or, separately, mental stimulation. This must be strictly controlled for an accurate result. The results will provide compelling evidence of what is happening within the test group and can be used as a starting point for other studies.
One of the major topics in recent years is the continuing rise in the need for appropriate dementia care. Alongside this, research is ongoing into ways of delaying or preventing its onset.
If the above study was applied to a specialised sample group, e.g. people with dementia who reside in care homes, would the results differ significantly? Understanding that there is a difference between the control group and smaller, targeted groups is important. What this data does not reveal are the reasons why there is a difference – and this could be even more important (whether the aim is care or cure).
Qualitative research aims to dig deeper. Understanding why something is triggered is the key to finding a solution. The research features human experiences and may include such factors as environment, attitudes and emotions, to build a knowledge bank of commonality and contradictory results.
And, just as the research addresses more complex and subjective issues, so too is the process of qualitative data collection. Generating robust qualitative findings requires specific skills and experience as there are pitfalls and a susceptibility to charges of being anecdotal or biased. Therefore a whole new set of principles are applied to qualitative research in order to demonstrate quality and rigour (the subject of a future article).
The rise of Big Qual Data
Qualitative research on a significant scale or Big Qual Data supports Big Data by presenting real humanised data.
- Not just what changes happened but why they happened;
- How your client base really feels about your company, healthcare interventions, products or services;
- Will your vision of the future meet the requirements of your potential clients?
In other words, with Big Data, past and present behaviour patterns can be used to project future trends. It’s there for everyone to see what is happening but to understand why it is happening adds another dimension – that is the advantage of Big Qual Data.
Governments use Big Data but those who aim to make a difference will use Big Qual Data. Pragmatists will use both – pooling the results for a greater understanding and using the power of complementary Big Data to forecast various outcomes.
Qualitative research has gained in popularity over recent decades. New and innovative techniques to capture copious amounts of qualitative data are on the rise (e.g. use of smartphones).
There has also been a growing emphasis on better understanding the patient – their opinions, their choices and their journey through the treatment process – what pharma refers to as patient centricity. Big Qual Data is a powerful new addition to the researcher’s toolkit waiting to provide the kind of insights needed within healthcare in which Big Data provides only half the story.