- 14th August 2018
- Posted by: Manolis
- Category: Blockchain
AI is consistently increasing the reliability and accuracy of medical image analysis through digital image processing, pattern recognition and machine-learning AI platforms.
The health care industry is continuously evolving, with greater focus on improving affordability, accessibility and efficiency of care and automation will play a critical role in this endeavor. While the personalized service of doctors and nurses cannot be completely replaced, automation can be integrated into their workflows to make healthcare delivery processes more efficient. The wide adoption and uninterrupted growth of Artificial Intelligence (AI) in health care is delivering benefits across the board, including excellent member outcomes, lower costs, easier provider workflows and member-centric treatments.
According to Forbes, the global AI market for health care applications is estimated to record a CAGR of 42% until 2021. McKinsey estimates that around 36% of the health care industry can be potentially automated. However, the potential is lower for health care professionals whose clinical expertise drives daily member interactions – less than 30% of a registered nurse’s daily activities can be automated, compared with 13% of a dental hygienist’s daily activities.
Digital technologies the new “wonder drug” of healthcare delivery
Mobile apps, wearable sensors, predictive diagnostics and telemedicine are easily accessible, efficient and cheaper forms of health care that have become popular today. The benefits of these automation techniques are resulting in accelerated investments in smart technologies to achieve several efficiencies, such as:
AI is consistently increasing the reliability and accuracy of medical image analysis through digital image processing, pattern recognition and machine-learning AI platforms. A handheld 3D-ultrasound tool, for example, can create real-time 3D representations of medical images, transmit them to a cloud service that identifies various characteristics and provides automated diagnosis. Similar AI-enabled tools will significantly impact the medical imaging diagnosis market and its growth.
By 2020, chronic conditions such as cancer and diabetes may be swiftly diagnosed by cognitive systems that identify typical physiological characteristics in scans and provide real-time 3D images. By 2025, AI systems may be implemented in 90% of the U.S., and 60% of global providers and insurance companies.
Robotics can automate ancillary and back-office services of providers, resulting in considerable cost/time efficiencies and higher reliability. Providers function as mini-logistics companies – they continuously move large volumes of material within their units. Despite their cost, quality and safety implications, logistics are not core to providers’ care-giving services. Nurses typically spend less than two hours of a 12-hour shift on direct member care, and the remaining on administrative activities. However, all this can now change due to robotics.
Innovative member guidance and engagement solutions have started automating major health care processes of Directly Observed therapy (DOT). AI-enabled medication adherence uses advanced facial recognition and motion-sensing software to observe treatment adherence. Member engagement can receive a big boost from automated check-ins and reminders.
In emergency rooms, doctors are leveraging the availability of abundant and instant data to get complete member information.
Wearable technologies, including blood pressure cuffs, glucose monitors and wireless scales, are improving member monitoring and reducing costs. When connected to wireless internet networks, these devices alert doctors and nurses about potential problems.
Process automation entails immense benefits
Health care organizations should become early adopters of automation to continue building efficiencies and overcome looming challenges (aging population, labor shortages and increasing costs). Process automation can be applied in specific areas of health care operations, such as, claims management, helping health care managers work more efficiently, effective administration, etc., to realize the following benefits:
Accelerating payer collections: Previously, collections involved contacting payer contact centers, IVR agents and claims agents. Now, health care providers are leveraging new, intuitive technology to accelerate claims resolution processes.
Streamlining coding operations: Medical coding is among the most complex processes – mistakes can be costly and time-consuming. The availability of several AI and Natural Language Processing (NLP) technologies is leading to 25% cost savings and 24/7 up-time in coding operations.
• Automated scheduling: Smart scheduling tools match medical specialties of nurses with members’ needs based on their proximity. Appointments are then scheduled for the best-matched nurse with the nearest member.
Automation needs proper investment and planning
Although automation in health care delivery and operations involve different processes, the basics of adoption remain the same. However, it needs proper investment and planning:
Management buy-in: Before implementing an organization-level automation strategy, it is imperative to seek management buy-in. Moreover, the Robotic Process Automation (RPA) team needs to be empowered. Prior approvals, requiring periodical review, should be taken for the entire project. A Centre of Excellence (CoE), which can push the automation agenda and overview implementation, should also be established.
Comprehensive process excellence: For successful organization-level implementation, process excellence must go beyond piecemeal improvements. It should necessitate macro-level enhancements in business operations and change management while ensuring adequate inter-departmental communication.
Design thinking: This iterative process seeks to understand users, challenge assumptions and redefine problems to identify alternative strategies and solutions.
Collaboration: For automation to successfully scale up, the entire organization must collaborate. Close coordination with the IT function will ensure a smooth transfer, from ideas (on paper) to actual execution (on workstations). The CoE should be equipped to troubleshoot problems during implementation.
To conclude, there is no single solution to implement automation across organizations. Companies need to determine the best strategy by thoroughly evaluating their options and deciding on a clear plan of action before scaling up their efforts.