Enabling resilience in healthcare with AI

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By Jennifer Mullen

The Covid-19 pandemic heightened pressure points in nearly every sector and industry, exacerbating the already crisis-level burnout that healthcare workers were struggling with. 

Today, hospital networks are struggling to keep pace with more patients requiring care from the professional healthcare workforce. Inflation, increased doctor-patient ratios, and a shrinking healthcare talent pool have stretched the medical sector to breaking point.

Even before the pandemic, hospital networks across the globe were investigating ways that digital healthcare technologies could improve access to healthcare, increase efficiency, and keep healthcare costs low. During the pandemic, the adoption of digital technologies accelerated and contributed to the resilience of healthcare systems.

Digital health technologies that leverage AI now contribute to hospital systems in meaningful ways, including cost reductions, improved access to healthcare, and resource optimisation.

AI-enabled clinical decision-making support for healthcare workers

Due to the healthcare talent shortages, practising clinicians see more patients than ever before and must balance efficiency with quality of care. 

Because of their automation capabilities and abilities to learn from large volumes of patient data and medical research, AI-enabled healthcare technologies are playing an increasingly large role in improving physical decision making when it comes to diagnosis, treatment, and ongoing care.

Equally if not more important are the benefits to clinicians. AI-powered clinical decision support can analyse patient data, medical literature, and the most recent best practices to provide physicians with evidence-based recommendations to diagnose, treat, and manage care for their growing number of patients. 

This allows clinicians to make better informed and far more personalised decisions regarding treatment planning and medication selection for patients. AI can also enable real-time alert automation to notify clinicians of critical findings, flag potential medication errors, and highlight missed opportunities for preventative care.

These digital healthcare technologies can make a meaningful impact on morale and job satisfaction for medical workers. They help healthcare workers make well-informed decisions while also reducing cognitive burden in physicians, improve the practitioners’ ability to see and treat more patients without sacrificing quality of care, and help to ensure better patient outcomes.

AI applications for healthcare networks

Healthcare supply chains are intricate, massive machines comprised of various mechanisms that orchestrate the delivery of services needed to ensure patient care. These supply chains are far more complicated than others because they include people and are significantly impacted by the needs of individual patients. 

AI applications for hospitals can connect different clinics, labs, and diagnostics to create a holistic view of the entire network, thus enabling improved coordination and communication. It can then perform predictive analysis of historical appointment data, demographics, and clinic capacities to forecast appointment demands with far greater accuracy. In turn, hospital managers can optimise resources and scheduling, resulting in shorter patient wait times. 

AI-driven chatbots and virtual assistants are also helping hospital networks to run more smoothly by automating appointment scheduling, prescription refills, and billing enquiries. 

And AI can perform routine, repetitive operational tasks and improve the accuracy and speed of diagnosing conditions like cancer much faster than humans. 

By reducing administrative burdens for healthcare staff, AI frees up time for healthcare workers to focus on patient care and clinical tasks while also making hospitals more cost-effective.

Telemedicine and remote monitoring

AI-enabled telehealth and remote monitoring capabilities increase access to healthcare, improve communication between healthcare providers, and improve the efficiency and cost-effectiveness of quality patient care. 

AI enables hospitals to share data and collaborate effectively on cases while preserving patient privacy. This facilitates more confidence in virtual consultations and enables more comprehensive care by allowing multi-disciplinary healthcare teams to coordinate patient care from different locations.

Rural patients often travel long distances for medical appointments and diagnostics and will do so out of necessity. However, once they have resolved their acute medical issues, these patients may not return for follow-up visits. The proliferation of wearable health monitoring devices enables physicians to monitor patients and make real-time, data-driven medical decisions without requiring the patient to be on-site.

AI and telemedicine are allowing practitioners to expand virtual care options throughout the treatment plan that can improve patient experience and their outcomes.

Barriers to adoption of AI in medicine

The benefits of AI-driven tools in healthcare are numerous, and their aid in building resilience is promising. 

However, there are many barriers to widespread adoption that device manufacturers and healthcare IT professionals must consider. Regulations, data privacy, interoperability, a lack of standardisation, and resistance to change represent significant hurdles that must be considered.

  • Regulatory compliance: It is crucial that devices and software used in healthcare settings be fully compliant with the rigorous requirements that regulatory agencies set to protect the privacy of patient data. Ensuring compliance approval is a costly and time-intensive but crucial process.
  • Data privacy: Medical data is highly sensitive and must be handled with utmost care. AI systems used in medical settings must comply with strict data privacy regulations; ensuring this security adds layers of complexity to the development and testing of healthcare software and devices.
  • Interoperability: Healthcare system infrastructure is complex. Interoperability issues make integrating AI systems into existing infrastructure challenging. Rigorous testing is required to ensure seamless integration with electronic health records, medical devices, and the plethora of other systems the hospital network relies on to ensure proper patient care.
  • Standardisation: AI is advancing more rapidly than the standardised protocols that evaluate it, resulting in complications for device and software testers. Without these established standards, it is difficult to evaluate or benchmark the performance of different AI models accurately.
  • Resistance to change: As with all industries, there is anxiety amongst practitioners about job displacement or changes in existing workflows. AI can be an invaluable tool for helping clinicians to deliver more personalised care and better outcomes, but first they must be educated and trained on how to use it effectively.

The world is increasingly relying on AI and automation to optimise resources and streamline operations. The healthcare sector has seen an explosion in technology that can aid practitioners in combatting burnout and optimise resources and operations in hospital networks. 

Jennifer Mullen is emerging technology solutions manager at Keysight Technologies

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