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While Adoption of Machine Learning Is Scaling Up In Medicine, it's Also Facing a Credibility Crisis - ResolveDatas
While Adoption of Machine  Learning Is Scaling Up In  Medicine, it’s Also Facing  a Credibility Crisis

While Adoption of Machine Learning Is Scaling Up In Medicine, it’s Also Facing a Credibility Crisis

Several studies have poured in claiming to demonstrate the ability of machine learning and artificial intelligence in healthcare to carry out the responsibilities with utmost precision. But only recently, the University of Cambridge came up with studies that showed that the application of machine learning and artificial intelligence had flaws.

Current Challenges for the Healthcare Industry

Looking beyond 2021, there are a few major challenges that the healthcare industry faces. Let’s take a look.

Big Data

Even though more and more healthcare data is being secured, it is scattered across multiple systems and parties, including patients, providers, and payers. There is no single source from which the providers can optimize the patient experience.

If healthcare organizations have to harness the power of big data successfully, they have to accept data-driven decision-making. The use of analytics has to be woven into the organization for developing trust in data. This way, insights can be used for supporting decision-making at an executive level.

To leverage patient data completely, healthcare organizations have to implement non-relational information technology to use data from various sources in various formats.

Payment Model

To reduce costs and improve service quality, there is a trend to determine financial incentives on the basis of the outcome of the patient instead of the service quantity.

Patients and bill payers are demanding a new payment model, such as global payment or bundled payments that encourage providers to coordinate service and facilitate preventive care.

But it can be difficult to implement these new models and monitor the process of an already-existing system. For instance, new metrics have to be defined for measuring the ROI and performance.

Cybersecurity

COVID-19 pandemic has shown how vulnerable patient healthcare data is. Growth in virtual health initiatives contributes to the serious increase in breached patient records.

Medical providers have to invest in proper safeguards to protect sensitive patient data.

Drug Pricing

Insurers, patients, and regulators all complain about the drug prices increasing. Pharmaceutical manufacturers claim that lower pricing will hamper product development. Meanwhile, all are grappling to find a pricing consensus as consumers struggle to keep with the rising prescription cost.

Several factors contribute to the drug pricing challenge. Almost 20% of the patients request a less expensive alternative when doctors issue prescriptions. The medical community has to come up with a framework to ascertain fair drug pricing. ​

Machine Learning can Help Some of the Challenges Faced by the Healthcare Industry

The growing number of applications of machine learning lets you get a glimpse of analysis, future data, and innovation work hand-in-hand to help outpatients.

Here is how machine learning and artificial intelligence in healthcare are proving to be useful.

  • Identifying Diseases: One of the primary applications of ML is diagnosing and identifying diseases that are otherwise considered difficult to diagnose.
  • Personalized Medicine: Personal treatments can be more effective when paired with predictive and health analytics. But it can also be used for better and further disease assessment and research.
  • Drug Discovery: Another main application of machine learning and artificial intelligence in healthcare is an early-stage drug discovery process. This includes the Research and Development technologies, such as precision medicine and next-generation sequencing that will help in finding alternative ways for therapy of multifactorial diseases.
  • Smart Health Records: Machine learning can ease processes for saving effort, time, and money. Document classification procedures with ML-based OCR recognition and vector machines techniques are eventually gathering steam.
  • Outbreak Prediction: Machine learning and artificial intelligence in healthcare technologies are being used to monitor and predict epidemics across the world. Presently, scientists have access to large amounts of data that is being collected from social media channels, satellites, website details, and more for its prediction.

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How Adoption of Machine Learning has Accelerated Over the Last Two Years?

The Machine Learning giants have already extended their hold in the healthcare machine learning and artificial intelligence industry. The techniques developed are being used and applied to pathology and predictive analysis.

Mad fever has aggravated as fast as the pandemic. Researchers are checking if synthetic intelligence can discover the secrets of COVID-19. Machine learning in this time of need can help in predicting patterns and further outbreaks.

Allied Market Research has predicted that global machine learning medical applications will only increase. According to them, it will reach $22.8 billion by 2023. It can lead to $150 billion annual savings for the healthcare industry.

Machine Learning On Its Own Cannot Solve All Problems

Machine learning comes with its own challenges. Healthcare is on the edge of entering a period of machine learning and artificial intelligence. Stakeholders from across the industry have to address a number of challenges when it comes to deploying ML in healthcare before they can reap rewards.

Check out a few challenges that the healthcare industry might be facing in regards to deploying machine learning and artificial intelligence.

  • Medical data is forbidden for access. As per a survey by Wellcome Foundation survey in the United Kingdom, 17% of public respondents are against sharing their health data with third-party commercial organizations.
  • The need for transparent algorithms isn’t only required for catering to strict drug development regulations. However, people need to understand how algorithms generate outcomes.
  • The healthcare industry needs to change its view on the importance of data and the way it can add value to long-term perspectives. For instance, pharmaceutical companies are reticent, when it comes to changing their product research and strategies in the absence of instant financial benefits.
  • There are a plethora of fragmented details between different databases that require more structuring. When the situation improves, it will culminate in advances to solutions of personal treatment.

What has to be Done for Machine Learning to Deliver an Impact in Healthcare?

Gartner expects that the world’s AI-based economic activity will increase from $1.2 trillion in 2018 to $3.9 trillion by 2022.

Here are a few things that the healthcare industry has to do to adopt machine learning.

  • It has to get a better grip of care endpoints. This will help ML achieve its full potential and improve outcomes.
  • Identify a defined use case with a requirement for improvement.
  • Avoid relying on a single vendor to offer all analytics solutions.
  • The healthcare industry has to foster the clinical acceptability of machine learning applications through interactive product development methods.

Where do We See Machine Learning in Healthcare by 2025?

It isn’t a secret that healthcare is expensive. Reducing cost is a prime driver of several healthcare initiatives and incorporating machine learning and artificial intelligence in healthcare isn’t an exception.

Artificial intelligence is designed for addressing real-world and specific use cases that make it easier to diagnose, monitor, and treat patients more accurately and efficiently.

The global market can face the challenge to synthesize large volumes of big data through ML methods and techniques, including semantic computing, deep learning, and neural networks.

The prime operational and clinical areas will include medical imaging analytics, clinical decision support, clinical trials, and drug discovery, patient management, and natural language processing.

As per a report by Tractica, Software developers who are looking to address the use cases are more likely to see annual revenue of $8.6 billion by 2025 and contribute $34 billion to software sales, consulting opportunities, and hardware installations in the AI market.

Healthcare organizations expect that machine learning medical applications products will offer actionable insights.

ResolveData is Helping the Healthcare Industry Create an Impact with Machine Learning

ResolveData is here to help your healthcare organization with the right tools and expertise. Shaping data to deliver improved outcomes, ResolveData uses data to empower you to drive actions for better care delivery and lower health costs. ResolveData helps you make data matter.

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