Visualization techniques can reveal relationships in data that might otherwise be overlooked.   However, a lot of organizations are still looking for that information management maturity level that will allow them to move to data mining and visual decision making techniques.

The recent consumer information boom brought us information technology advantages that we currently enjoy – but effectively employing visualization techniques still demands having accurate and complete data, and a willingness to explore information through the lens of all other actors within the information ecosystem.  

For instance in healthcare, this means simultaneously scrutinizing data from the physician, payer, provider and patient primary perspectives. However recent pushes for Comparative Effectiveness platforms and development of disease registries demand additional scrutiny at deeper levels such as : diagnosis, disease, prevalence, episode of care, pharmacy, drug, procedure,  employer, geography, service utilization and cost, race, gender, employment status, genome, etc.   

In practice, this means ensuring that our data warehouses dimensionalize not just the organization’s perspective of facts, but also the perspectives of all other important actors involved.  It’s no longer good enough to merely capture and present, for example, Medical Procedures by Patient, Physician, Hospital, and Time – because this veils the patient perspective.   

Many other health care innovations that in the past were extremely difficult to achieve, are now becoming a step closer to reality.  For instance, by enabling a thorough real time longitudinal monitoring capability at patient level, newer initiatives such as disease prevention or disease management could start fine tuning the medical management business toward better containment or controlling medical costs.

In addition, a combination of system generated medical records, patient entered health data and 3rd party behavior characteristics could be brought together to predict disease onsets or to help clinicians detecting patterns, symptom associations or hidden comorbidities.