How AI-Driven Healthcare Data Management Impacts the Future of Healthcare
March 24, 2020
Machine Learning and AI Transform Healthcare Data Management so Healthcare Stakeholders Can Achieve Lower Costs and Better Patient Outcomes
Healthcare is a rapidly changing industry. Big data, artificial intelligence (AI), and machine learning – these terms have become mainstream in many industries and healthcare is no exception. AI applications are increasingly becoming a part of healthcare to the point where there have been rumblings about AI possibly taking over some healthcare jobs. But is this realistic? Should healthcare stakeholders all just embrace AI in healthcare?
What is Artificial Intelligence (AI)?
Artificial intelligence is a collective term used in reference to multiple technologies that aim to mimic human cognitive functions, making machines able to sense, understand, act, and learn. Some AI technologies that are important to healthcare stakeholders and the healthcare industry are:
- Machine learning – neural networks and deep learning: statistical techniques used to fit models to data and to ‘learn’ by training models with data.
- Natural language processing (NLP): making sense of human language with applications such as speech recognition, text analysis, and translation.
- Rule-based expert systems: based on “if-then” rules.
- Physical robots: actual robots such as surgical robots.
- Robotic process automation: computer programs used to perform structured digital tasks for administrative purposes.
AI in Healthcare – Past, Present, Future
Healthcare is a data rich industry which provides fertile ground for applications of AI in healthcare data management as well as other aspects of healthcare. In the past, AI technologies in healthcare were mainly algorithms or tools that complement a human. The rule-based expert systems of “if-then” rules were dominant in the 1980’s and for some time beyond. However, inefficiencies in such systems that result when the number of rules become too large, are causing them to be replaced by machine learning algorithms.
Today, AI in healthcare can truly augment human activity and is taking over tasks ranging from medical imaging to risk analysis to diagnosing health conditions. AI is being used to discover links between genetic codes, to power surgical robots, and to maximize hospital efficiency. As a result of this, growth of AI in healthcare is exploding and is estimated to reach $6.6 billion by 2021. An analysis of the market found that when combined, key clinical health AI applications can potentially create $150 billion in annual savings for the United States healthcare economy by 2026.
In the future, AI will play a critical role in precision medicine, which is the direction in which healthcare delivery is headed. AI is expected to improve areas such as diagnosis and providing treatment recommendations, imaging analysis, healthcare data management, patient communication, and capturing of clinical notes through speech and text recognition.
According to a PWC report, Why AI and Robotics will Define New Health, AI in past decades have focused on innovations in medical products (equipment, hardware, and software) that deliver historic and evidence-based care. The present decade has seen a rise in medical platforms focused on real-time, outcome-based care in the form of wearables, big data, healthcare data management, and health analytics. Healthcare stakeholders should be aware that it is believed that the next decade will see an increase in medical solutions involving robotics and augmented reality, which will deliver intelligent solutions for both evidence and outcome-based health and focus on collaborative, preventative care.
Uses of AI in Healthcare
“AI is being used to discover links between genetic codes, to power surgical robots, and to maximize hospital efficiency.”
One description of AI is that it “simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost.” AI technologies have found a multitude of uses in healthcare such as:
- Efficiently and accurately diagnosing conditions and reducing errors
- Treatment recommendations
- Development of medicines
- Streamlining the patient experience
- Data mining and healthcare data management
- Robot-assisted surgery
- Administrative applications
With all these uses and more, AI in healthcare is here to stay and healthcare stakeholders who want to remain relevant and thrive in this space, need to make the necessary moves now to incorporate AI into their operations and optimize healthcare data management.
Impact of AI on Healthcare Data Management and Healthcare Stakeholders
The amount of data available in healthcare is staggering. Data sources include electronic medical records (EMRs), insurance claims, clinical trials, drug research and development, and patient generated health data. All these points of data have the potential to change the healthcare landscape if properly managed and leveraged accordingly. However, without proper healthcare data management, valuable data can become lost among the large volumes of data points available; this is where AI can help healthcare stakeholders.
“Healthcare could save up to $100 billion a year by utilizing big-data crunching algorithms backed by AI to inform decision-making and realize efficiencies in clinical trials and research”
It is estimated that the industry could save up to $100 billion a year by utilizing big-data crunching algorithms backed by AI to inform decision-making and realize efficiencies in clinical trials and research. There are companies that are leveraging the power of AI to assist healthcare stakeholders with healthcare data management and improving healthcare delivery. Tempus is one company that is using AI tools to collect and analyze the world’s largest library of clinical and molecular data to drive precision medicine. Its AI-driven data are being used in cancer research and treatment.
KenSci is another company that is using the power of AI and big data to improve healthcare. Their risk reduction platform aggregates data from existing sources such as EMRs, claims, and financial data to help uncover clinical, operational, and financial risks. It can predict who might get sick and the drivers behind healthcare costs; it can also provide solutions to these problems.
H2O.ai uses AI to analyze data throughout a healthcare system to mine, automate, and predict processes. It has been used to foresee ICU transfers, improve clinical workflows, and even pinpoint a patient’s risk of hospital-acquired infections.
AI-enabled applications are also impacting healthcare data management practices. Computer-assisted coding (CAC) is on the rise, utilizing NLP to read and interpret clinical documentation in patient health records and suggest applicable diagnosis and procedure codes. For CAC to be fully adopted and optimal efficiencies realized, the medical coding workflow will have to be re-engineered. As machine learning becomes more integral to reading images for diagnosis, the requirement for a physician to interpret an image may become less necessary. As such, medical coding and reporting guidelines and standards will need to be adjusted to account for AI applications.
Benefits of AI in Healthcare
AI has proven to be a boon to the healthcare industry. Some of the benefits from incorporating AI technologies in healthcare include:
- Faster, earlier, and more accurate diagnoses
- More efficient data mining and healthcare data management
- Lowered costs
- Improved patient outcomes
- Enhanced patient engagement
- Healthier behaviors and proactive lifestyle management with wearable technology
- Better understanding of the patient’s condition and improved management resulting from increased insight of the healthcare team into the day to day lives patients
- Improved clinical decision-making
- Reduced administrative burdens and improved efficiency in managing administrative tasks
- More efficient drug research and discovery process potentially cutting both the time to market for new drugs and their costs significantly
AI technologies are already proving to be a game-changer in healthcare and there is still a huge potential for them to do even more. Many companies including big names such as IBM and Google are investing heavily in AI for healthcare. We are on the cusp of many discoveries and breakthroughs as the potential of AI is fully realized in the healthcare industry.
Challenges/Pitfalls of AI in Healthcare
While the advancement of AI technologies in healthcare is exciting and offers numerous benefits, like everything else, it is not without challenges. Some healthcare stakeholders prefer to take a cautious approach, with some wondering how far is too far. Below are some of the challenges/pitfalls facing healthcare stakeholders and the evolution of AI in healthcare:
- Potential biases in results based on the data used to create algorithms and ‘train’ the AI technology.
- Transferability of algorithms ‘trained’ in one setting or population to another.
- Data ownership, confidentiality, and consent – who owns the data? Who is responsible for healthcare data management? How will patients’ data be kept confidential? How do healthcare stakeholders handle the need for consent in research?
- Ethical issues and professional responsibility – who is responsible when a patient is misdiagnosed?
- Legal risks and regulatory issues – at present, regulations are falling behind the explosion in AI development and use potentially creating a legal and regulatory nightmare for healthcare stakeholders.
Healthcare is experiencing an upsurge in the development and application of AI technologies. These technologies are very beneficial in that they can perform tasks that are usually done by humans in less time and at much lower costs, simplifying the lives of healthcare stakeholders including patients, doctors, and hospital administrators. Healthcare data management is one area that has benefited significantly from AI and has resulted in better patient outcomes and cost savings for healthcare stakeholders. However, there are some serious challenges to the use of AI in healthcare that need to be overcome. While AI might not take over healthcare jobs, these challenges need to be managed before we can all completely embrace the full potential of AI in healthcare.
To find out more about how wearables and patient generated health data are part of the bigger AI picture in healthcare, contact the healthcare technology experts at Acuma Health.
Healthcare Data Management, Healthcare Stakeholders, Healthcare Technology, High Risk Patients, Patient Generated Health Data