Healthcare marketing analytics may identify healthcare service bottlenecks, not just manage call centre wait times or evaluate marketing campaigns. Link analysis marketing analytics involves tracking populations as they move from point to point in a system. It requires an ability to create hierarchical buckets – population segments – that allow an analyst to focus on the ‘% of the population’ as it moves from point to point in a system. And it might reveal points in the healthcare system where patients must wait longer than required for treatment. Findings from a link analysis of healthcare patient data may guide decisions to improve the flow of patients from one point of care to another.
Every Canadian has a health card (http://www.ontario.ca/en/services_for_residents/STEL02_186323) that he or she must present when visiting a family physician, healthcare specialist (eg. Neurosurgeon), MRI or X-Ray clinic, hospital, medical centre, or other healthcare provider. Data warehouses store details of every patient’s healthcare visit (eg. based on his or her health card number) to a healthcare provider (eg. each has a unique provider code) who was provided treatment. The healthcare provider who made the referral, the date on which it was made and to whom, are also captured. Link analysis marketing analytics may be applied to this data to track patients as he or she moves from one provider to another through the healthcare system.
As an example, let’s assume that there are the following fields of information about healthcare visits for 100 patients for a particular month:
- patient identifier (HEALTH CARD #)
- date/time stamp of visit (VISIT DATE/TIME)
- code of the healthcare provider visited (PROVIDER VISIT)
- reason for visit (VISIT REASON)
- code of healthcare provider to whom patient is referred (PROVIDER REFERRAL)
A marketing analytics data exploration exercise has revealed that one or more of the following healthcare providers (PROVIDER) were visited by at least one or more patients during the month in question:
- Hospital (1111)
- Medical Centre 1 (2345)
- Medical Centre 2 (2346)
- Physician 1 (0023)
- Physician 2 (0024)
- Laboratory 1 (3346)
A preliminary analysis of the raw visit data may also indicate that several patients have visited more than one healthcare provider, while others may have visited multiple providers on the same day.
Before a link analysis may be conducted, the PROVIDER VISIT and PROVIDER REFERRAL variables must be transformed from row to column format for each patient. For example, ‘Medical Centre 1′ must change from being a value in the PROVIDER variable to a boolean variable, and multiple visits to Medical Centre 1 require matching variables to capture this activity. ‘Medical Centre 1a’ and ‘Medical Centre 1b’ variables will capture that a patient made two visits to Medical Centre 1, whether on the same day or on different days. It is critical that the VISIT DATE/TIME field values be used to order the multiple provider variables from the first to last date/time of visit. The process must be followed for each of the other 5 healthcare provider values in the PROVIDER variable.
Once this data shaping has been completed, the newly-created boolean variables must be rolled-up to reflect the ‘% of the population’ as it moves between each healthcare provider for treatment. Viewing link analysis results graphically might lead us to conclude that there are bottlenecks in the healthcare system or providers who must have their patient load reduced and shared with others.
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