SpotDy BigAI for Health Care

According to a McKinsey study, healthcare providers ignore 90% of the data they generate. This is because healthcare companies are ill equipped to face the challenge of not only storing data, but also accessing it and making it usable. Any Healthcare company that harness the data to the fullest extent can reduce costs, increase revenues and improve treatments thereby improving patient experience and care.

What SpotDy can do for Health Care?

Cost cuttings

With SpotDy, healthcare process can be streamlined, including coding, billing, and supply-management practices. This pinpoints where dollars are best spent and where they can be saved.

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Track patients health behaviours

With SpotDy, healthcare companies can adopt evidence based practice to treat patients culled from data pools of past patient experience. This helps place the right patient with the right provider.

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Treatment improvements

With SpotDy, healthcare providers can link health data to patients shopping histories, social media, and other information to gain window into the patients daily health behaviours thereby reducing treatment costs.

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Use Case

A large nonprofit Hospital in the state of Massachusetts found that they could not leverage the huge data to accurately measure the size of their patient care population and the composition of the patients. Not knowing exactly the customer size and customer composition is hindering the hospitals ability to formulate strategic direction, its clinical policies and key strategic initiatives. Moreover, the problems were exacerbated with different divisions of the hospital having different business models. This presented a huge data challenge to combine data generated from different divisions. The hospital recognized the need for a tighter integration of different divisions to serve its patients better.

The needed engineering to combine data across systems and organizations to solve the above problems was stymied by data collection methods adopted by each division, data inconsistencies, incomplete population of fields and other types of data quality problems between each system. These issues made it impossible to provide the analytics the Hospital senior managers needed to take strategic decisions.

The Hospital resorted to Big data analytics to solve the problem. Big data technologies have the capacity to combine data across varied sources, inconsistent data and apply advanced analytics. Using Big data, the Hospital solved the above issues by creating an integrated way to view the patient information, encounters, providers, and hospital locations. The data integration helped the hospital improve its clinical outcomes, growth in patient population and ultimately maximize its return on its data. The Hospital also gained a reliable measure of its primary care patient population, active patients, unique patients and unique service providers.


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