A growing and ageing population, rising rates of chronic diseases, and higher labour costs are challenges that healthcare organisations are aiming to overcome by increasing operational efficiency and improving customer insights.
Patients complain about a lack of customer service, negative front desk experiences, and issues relating to the siloed nature of their medical history – meaning they have to repeat their symptoms, health checks, prescriptions and other details. At the same time, the fragmented data makes it harder for clinicians and support staff to get an understanding of the patient’s experience, and also leaves health insurance companies unable to properly assess patient risks.
Artificial Intelligence technologies can yield dramatic improvements in data collection efficiency, interpretation and related decision-making. Consequently, the AI in the healthcare market is forecasted to grow from $1bn in 2017 to $28bn in 2025, according to BIS Research.
BlackSwan Technologies’ ELEMENT™, an AI-enabled SaaS offering trusted by leading organisations around the world, can help healthcare enterprises to:
- improve customer service through a better patient onboarding and responsiveness
- enhance the ability for insurers to assess patient risk
- detect and prevent healthcare fraud
- obtain a 360 view of the patient to improve their journey
Below are examples of healthcare business cases where AI-powered applications based on ELEMENT™ offer exceptional value.
Customer service enhancement
Improved patient flow makes for better care and improved satisfaction
ELEMENT™ enables healthcare organisations to streamline the onboarding process, reducing costs and staff time. Automated patient profiles are built using a variety of patient information sources, thereby negating the need to repeat questions or examination steps. Document parsing and fact extraction based on advanced Natural Language Processing can help to decrease data and document collection efforts and subsequently improve regulatory compliance and reduce errors. All of these lead to better patient journeys and customer experience.
Healthcare providers can use the data to recommend a variety of services beyond doctor’s visits, such as at-home nursing, reminders about medicines supplies and therapist recommendations.
Patient risk management / proactive care management
Better estimation of patient risk leveraging informed data for proactive measures
ELEMENT’s comprehensive data set compiled during earlier onboarding and interactions with patients can be analysed using machine learning technology to build a dynamic, up-to-date risk assessment for each patient, which allows a healthcare organisation to visualise the most important risk factors and recommend treatments and services to the patient. In addition, ELEMENT™ can combine these risk factors, with estimated actuarial calculations in order to offer precise, tailored insurance programmes and model the likely financial outcomes.
Thwarting healthcare fraud
Defending against fraudulent activity through deep learning
ELEMENT™ uses a cognitive computing approach to enable fraud detection strategies that are not possible with most existing systems. Deep learning is used to identify likely patterns of fraudulent behaviour by one party or multiple parties in collusion. Fraud monitoring is continually updated during data processing and analysis, with recent behaviour compared to historic activity.
A Knowledge Graph that represents the relationships between patients, care providers, visits and financial transactions can help to highlight allusions to improper behaviour such as healthcare providers charging for procedures that weren’t carried out, unbundling charges and the provision of unnecessary treatments.
A single 360 view of the patient
A single source of truth to help healthcare staff to make informed decisions
ELEMENT™ incorporates structured and unstructured data (such as call transcripts and emails) into a flexible knowledge repository from patient information sources.
Using ELEMENT’s entity extraction and disambiguation technique, organisations can identify that patient data stored with slightly different names and in different data stores belong to the same patient and build a thorough patient profile with aggregated provision points. For example, a hospital may store data and test results using a social insurance number, while a small clinic may be storing test results using a patient’s name and address. Thanks to advanced entity recognition and disambiguation, this data can be reconciled, removing the need for complex and incoherent data integration projects.
All this is paired in ELEMENT with a rigorous data privacy framework that can assign access privileges by department, role or by the individual, all information sharing authorizations and a complete audit trail of data modifications.
ELEMENT serves as a platform to deploy AI-powered apps throughout your healthcare enterprise, in such related areas as customer interaction and services personalization with ELEMENT of Marketing.