Why Should Healthcare Leaders Focus On Human-Machine Partnerships?

AI dependencies dominate clinical effectiveness.

SwissCognitive Guest Blogger: Ethan Millar – “Why Should Healthcare Leaders Focus On Human-Machine Partnerships?”

Human-machine partnerships with AI and ML continue to trend and dominate clinical effectiveness. It supports new drug trials, APIs, and lab R&D increasing business revenue. It also has the potential to be sensitive towards patient care. The biggest transformation lies in a pivotal change: instead of ‘responsiveness’ the focus shifts to ‘prevention.’

AI technology facilitates the sensitive healthcare industry in diverse ways, primarily relying on emotional intelligence to succeed. For example, healthcare systems in developed nations find it an uphill task to keep the growing, aging population away from medical collapse. AI’s suitability and capability work wonders for patients’ physical and mental progress.

Healthcare leaders should make serious investments in human-machine partnerships to improve clinical effectiveness. Automation also supports revenue generation, organized operations, and handling of medical records. AI can benefit hospitals, specialty clinics, labs, and pharma companies.

Key drivers for successful AI dependencies

How is AI evolving in healthcare outcomes?
AI as a ‘human’ partner and powerful tool
Clinical and admins benefit from automated features
Support of virtual healthcare assistants
Digital twin models
Convergence of mental and physical well-being

How is AI evolving in healthcare outcomes?

Humans have been using machines for a long time to save time and energy. Various tools have features with unique strengths and weaknesses that can be harnessed. In the healthcare industry, the human-machine partnership is being tested with the emergence of AI. The tools are being developed to augment better medical outcomes. They are already capable of transforming the operations of the medical fraternity. This disruption leads to more innovation for humans and machines to operate successfully.

How will man and machine collaboration become more effective in the coming years?

The secret lies in safety, accessibility, and affordability for patients at the core of this industry. The following table shows its effectiveness as AI evolves to deliver better patient care. Pharma and life sciences are likely to grow rapidly with AI technology.

MAN	MACHINE LEARNING & AI	RESULTS
Healthcare through digital transformation	Automation	Collaborative outcomes
Helping clinical trials to succeed.	With training supervised and unsupervised learning is possible.	No machine functions alone but with the help of human intervention.
Offering patient-oriented care.	NLPs offer statistical learning.	Machines can be used exclusively for diagnosis, x-ray scans, and screening of medical conditions.
Well-organized for larger trials	Use of AI chatbots and Voice assistants. Also called virtual assistants are excellent for nursing.	A chatbot can help a patient to learn how to use the injection (as in the case of diabetes).
Digitization reduces human errors.	Robotics in medical sciences is a boon.	AI can help unlock big data in clinical trials.
Maintaining records and insurance claims.	Robotics are now reaching out through social companionships, rehabilitation, surgical robots, and assisted living	Optimizing healthcare systems with insights.
Optimized workflow across systems.	Increasing tools and features adoption.	Improving patient outcomes and making treatments affordable

AI as a human partner and powerful tool

AI tools add value and benefits primarily in two areas, keeping in mind patient care:

Clinical section
Administrative section

Big data includes a plethora of medical case studies involving genetics and innovation via technology. The most significant trends in the healthcare industry vis a vis big data include studies focused on:

Patient-oriented care– it begins with validation from researchers as they conduct trials in an organized way. Systematic data mitigates errors, increases quality control of services, reduces costs for patients from false readings or reports, and improves payment structures.
From drug development to commercialization– experts are outsourced for extensive trials in clinics, specialty hospitals and government facilities. Compliance with rules regarding time-to-market products is critical.
Pooling of unstructured data- it includes prescriptions, diagnostics, lab tests, patient care records, and insurance claims.

Clinical data management is bringing automation with:

Complete data sharing between different medical experts to treat patients.
Reliability of records during processing from one format to another.
Fewer problems in large clinical trials are expected with the automation of data.

Multiple IDs can be created to make results more authentic during the crucial R & D process. The actual authorization is limited to a few personnel only. It ensures the trial design is secure and safe. Any clinical failure is rectified with the expertise of specific professionals involved during the audit. This drastically reduces false claims during trials with guidelines provided to meet future healthcare demands.

There are challenges in both areas while adopting AI capabilities. With future progress, the gaps can be bridged for the following:

Data access is only for the authorized staff.
Treatment disparity due to bias in patient trials. The disproportions are reduced.
AI tools are difficult to control when evaluating results. Thus, transparency is compromised. New tools will create clarity.
Privacy risks are never ruled out. As data is available to more organizations globally.
The adoption of AI tools is restricted due to development by different parties. They are a liability or restriction for continued use.

There are other areas where innovative technologies are useful and complement human-machine partnerships.

How do virtual healthcare assistants help?

Due to the pandemic, many patients started seeking online help for medication, medical opinion, and continued care. Many clinicians are satisfied with virtual assistants to bond with patients. They can offer advice for treatment, diagnosis, and prescribing medicines. The assistants talk to patients and answer questions.

They offer answers and communicate with them. It reduces the need for patients to visit the doctor when they need urgent attention. AI chatbots can schedule and set up appointments. The new versions provide companionship to patients. It is useful as it keeps the patient mentally engaged and at peace!

Digital twins: modeling organs virtually

There is much hype regarding the emergence of ‘digital twins’ in the healthcare industry. Being unique, they provide digitized, personal, and custom options for the medical fraternity to work. This concept works on creating a ‘digital twin’ of a ‘person,’ ‘organ,’ or a ‘tool.’ It is ideal for testing drugs and wearable medical devices for patient usage. Thus, they pave the way for man and machine to deliver better results with the least effort.

In a hospital environment, they are created for lab results, studying computer models, and understanding the environment. While it is understandable that a hospital replica can be made for research and improvement, modeling human organs is challenging. With AI capabilities, it is possible. A treatment plan or a critical surgery can be conducted with the help of digital twins. In the future, more tools will be introduced to understand its benefits in real-time surgery with robots.

Convergence of mental and physical well being

While there are rapid advances in AI tools for physical health, the other side, mental health, cannot be ignored. It is more critical to take care of the patient’s mental health while offering treatments. It also assumes importance for the growing aged population in several developed nations. They need to survive with the least human interference.

New-age patients have different expectations from their healthcare service providers. Their needs are getting more complex. Converging man and machine to address this reality is a challenge. Being proactive can set the pace for delivering robust solutions with the combination of the emotional intelligence of doctors and the capabilities of artificial intelligence.

Is AI a wake-up call in this realm?

Depression, anxiety, and loneliness form the deadly triumvirate that needs immediate attention. Globally, many adults suffer from mental illnesses while tackling long-term physical ailments. Depression is also a major contributor to diseases that take long to cure. AI tools like chatbots can detect depression risks in patients.

Early detection can provide better treatment options and get help from nurses, relatives, and volunteers.
AI bots can provide personalized recommendations, cures, or therapies. They collect feedback and prompts to offer advice.
Such AI tools are a boon in robust organizations and are effective for everyone’s well-being. Restrictions on privacy and access give confidence in people to interact with machines. This is yet another novel human-machine partnership that can be explored.
In organizations where diversity creates issues or there is sexual harassment, it can be mentally agonizing. AI can create a safe environment to express and get support.

With the help of AI Consulting solutions, the burden on healthcare systems will be reduced if organizations focus on the mental needs of their employees. If you are a part of the healthcare industry as a policy-maker or decisions taker the above content should be adopted with the best practices.

Conclusion

Where to start AI adoption in healthcare?

As a leader, where would you start your AI journey to ensure that the man and machine work successfully? Begin by opting for low-risk and high-impact solutions. Automation with data and smaller clinical trials is the best way forward. Move slowly and see the results before investing in this realm. Getting familiar with this technology will pave the way to use chatbots, virtual assistants, and digital twins more effectively.

Invest in clinical documentation and its time-consuming analysis. With AI’s predictive tool features the efforts are not wasted and present accurate results. Be a part of this revolution and meet the needs of patient care.

About the Author:

Ethan Millar is a technical writer at Aegis Softtech especially for computer programming like artificial intelligence, emergency technology, Big Data, data analytics, and CRM for more than 8 years. Also, have basic knowledge of AI and technology are vast fields with numerous experts contributing to various aspects of research, development, and application.