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Creating a Seamless Digital Experience for Patients

Feb 01, 2021

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Part of Life Science Series

By Monstarlab

In healthcare, along with the rise in demands from patients and consumers, rises the costs of meeting those demands with expensive methods and innovations. Across the globe, healthcare systems are challenged by a continuous change in consumer expectations despite consistent efforts, especially with the COVID-19 pandemic dramatically changing healthcare demands and conditions. Accounting how industry leaders reactively implement various technologies strung together to make cost-effective strategies, experts have gathered substantial and pivotal insights on what it takes to maximise value for patients while achieving the best results for the lowest costs. 

In this article, you will learn:

  • What pandemic-sensitive strategic implementations make for a well-structured end-to-end patient/customer journey
  • What are the advantages of employing various digital service and customer support strategies within the industry
  • How these strategies can fit into your plans and executions

Assess patient needs and filter cases promptly

Assessing health needs digitally is a comprehensive process – reading patient-reported conditions and fitting information against set criteria is simply not enough. However, that should not translate into an entitlement to waste a patient’s precious time on tedious investigations when their concerns clearly imply they should be seen by a physician, especially with the high stakes within a fast-spreading pandemic. 

From the onset, it is vital to incorporate the concepts of triage and capacity to heighten the effectiveness and advance the speeds of necessary health interventions. Making service limitations explicit and setting boundaries on the benefits consumers can pursue in specific platforms makes drastic changes in service efficiency and accuracy. 

According to experts, the use of rule-based automation to separate and properly sort cases does not only benefit enterprises in increasing service proficiency and high consumer ratings [1]. It also influences the accuracy of priority setting for providers and expectations setting for patents – suggesting grounded proportions of resource rationing and promoting more systematic and cost-effective digital services. 

Balance machine and human assistance options

Artificial Intelligence (AI) is increasingly becoming known for its unparalleled augmentation of human abilities. From disease diagnosis to conversing and attending to consumers, this technology is changing the way healthcare providers fundamentally care for patients in a surge of patient populations. However, alongside popularity amongst executives, significant increase in so-called “digital fear” has also been reported by several studies [2]. This brings to light reasonable fears of unemployment for workers and of unreliable “machine opinions” in lieu of professional input as an outcome of the shift in customer support machinery.  

In order to address such fears and fully exhaust the potential of AI technologies, it is important to highlight how such are used to complement human abilities and not to dehumanise health services or replace the workforce. By using AI to perform basic and routine tasks, and employing workers to take on more complex work, providers can demonstrate and take advantage of harmonious human-machine collaboration. 

According to recent research with over 1,500 participating companies, the majority of organisations in the industry are applying AI to automate day-to-day processes, and these enterprises are enjoying the most significant performance improvements in the sector and accrue better customer feedback when humans and machines work together [3]

Personalise patient care

Despite the generality of doctrines and regulations within the practice, healthcare should never be treated as a field catering to homogenised audiences. Providers need to understand the importance of seamlessly combining the  efficiencies and quality of standardisation with possibilities and occurences of specialised cases. That being said, the use of Artificial Intelligence (AI) and Machine Learning (ML) for service customisation purposes is highly recommended.

Research highlights the abilities of AI and ML to create unique experiences and align custom-fit offerings to meet the demands of healthcare consumers. From matching concerns to accorded services and patients to physicians with compatible specialisations, credentials, and schedules,  to important adjustments in profile information and even interface design, the combination of the said technologies personalises healthcare on deep and rewarding levels while catering to masses of users at the same time. In fact, according to reports, digital healthcare tools and softwares forwarding personalisation are rewarded with more downloads and a 75% higher average of Play Store and App Store ratings compared to more rigid programs [4].

Key Takeaways

  • Setting boundaries and expectations for offered digital services is crucial to efficient service and support delivery in a demanding global health crisis; this not only benefits patients by reducing the time between inquiry and intervention, but also allows providers to screen serviceable demands instantaneously.
  • Increased integrations of technology entail positive dynamic disruption when well-allocated. Reliance on technology for certain processes (especially those of larger scales and that require faster actions) does not automatically threaten the livelihoods of professionals in the sector and actually provides them more and better work opportunities while significantly improving their performance – subsequently amplifying service value for patients.
  • Complying to standards and regulations should not translate into a one-size-fits-all approach to healthcare. Personalisation is crucial to fostering a culture of accuracy and inclusivity in the industry and should be employed in any and all health transactions.

Endnotes:

[1] National Institutes of Health, “Development and importance of health needs assessment”, 1998
[2] AdventHealth, “Personalizing Patient Care: A Reference Guide for Healthcare Professionals, 4th Edition”, 2019
[3] Harvard Business Review, “Collaborative Intelligence: Humans and AI Are Joining Forces”, 2018
[4] IQVIA Institute, “The Growing Value of Digital Health”, 2017

Other references:

National Institutes of Health, “Why digital medicine depends on interoperability”, 2019
Forbes, “Healthcare In The Age Of Personalization”, 2019
Statista, “Cost decreases from adopting artificial intelligence (AI) in organizations worldwide as of 2019, by function”, 2019

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