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There are more than 12 ares within a healthcare institution where data science is applicable. An overview of these areas, through a data strategy and roadmap, will further help institutions to exploit the benefits of data science.
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AI can be used in almost every domain within diagnostics, from heart failure to psychiatry. Combining patient data (symptoms, nutrition data, movement data etc.) with the right techniques (image recognition, deep learning) leads to models and tools that support the specialist in his or her diagnosis. Aggregated disclosure of diagnoses across institutions is the next step in this development, to disclose knowledge.
These models are the basis of a large number of savings through a more focused deployment of people and resources. Think of:
- Short term demand for care: predicting future peaks in the incoming flow
- long-term demand for care: important for the amount of resources (real estate, staff)
- Diagnosing behavior of specialists: determine which requested examinations are not logical for the diagnosis/transaction concerned
- Prediction of preventable deaths
- Predict re-admissions
Multi resource planning
Double digit savings can be achieved in every healthcare institution through the use of process mining and other supply chain optimization techniques. Value can be achieved in both the planning of the operating rooms and the personnel planning. Pipple has experience with multi resource planning optimization.
Cognitive chatbots can be of value when answering incoming questions from patients and family. In this context, unlocking knowledge in a cognitive environment can also provide value because knowledge can be accessed more quickly and can be maintained more easily.
Health care institutions often have large numbers of employees. Most institutions also have significant absenteeism percentages and difficulty in recruiting new employees. Data science can be helpful in these domains, such as:
- Prediction of absenteeism and burnout
- Matching supply and demand
- Chatbot for staff questions
- Learning systems
Healthcare institutions must, among other things, match large quantities of invoices with underlying healthcare contracts. There is a lot of mismatch. Automatic matching of invoices with underlying agreements from healthcare insurers, can save a lot of manual labor costs. Also analysis of deviations can add value.
Other solutions for value creation:
- Predictive maintenance
- Keeping patients at home for longer
- Procurement analytics