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.

Adverse Drug Reactions vs. Adverse Drug Events – Detect and Avoid for Patient Safety

We are experiencing a health craze. Everybody is becoming health conscious and there is an abundance of herbs, supplements, lotions, etc. that people are using in their quest to rid themselves of, and prevent, ailments.

In addition, there are numerous prescription medications (almost 6,800) and countless over-the-counter drugs available in the US market. The use of so many substances in health care opens the door to possible interactions between substances, resulting in medication adverse effects. In the clinical space, terms such as adverse drug events (ADEs) and adverse drug reactions (ADRs) are used to describe some of the possible medication adverse effects that can result from drug use. But does the average person understand the meaning of these terms?

An adverse drug event (ADE) is defined as “an injury resulting from medical intervention related to a drug.” An ADE results from harms caused directly by the drug itself and include medication errors, ADRs, overdoses, and allergic reactions. An ADR, on the other hand, is a harmful and unintended response to a drug at normal doses and during normal use.

The Office of Disease Prevention and Health Promotion, part of the US Department of Health and Human Services, has named ADE prevention as an important patient safety priority in its National Action Plan for Adverse Drug Event Reporting. It noted that ADEs accounted for an estimated one-third of hospital adverse events and approximately 280,000 hospital admissions annually. The ADE Action Plan was established to coordinate multiple stakeholders and align Federal efforts in identifying common, preventable, and measurable ADEs that may result in significant patient harm. The goal is to jointly work towards reducing patient harm from these specific identified ADEs nationally.

Three types of ADEs were selected as the high-priority targets of the ADE Action Plan as they were identified as being common, clinically significant, preventable, and measurable. These are:

• Anticoagulants: primary ADE of concern – bleeding
• Diabetes agents: primary ADE of concern – hypoglycemia
• Opioids: primary ADE of concern – accidental overdoses/over-sedation/respiratory depression

The World Health Organization lists has reported that as many as 4 in 10 patients globally are harmed in primary and outpatient health care. Up to 80% of harm is preventable. The most detrimental errors are related to diagnosis, prescription and the use of medicines. As such, healthcare organizations are urged to utilize the ADE Action Plan and implement strategies to prevent medication adverse effects, especially from the three priority types of ADEs identified. Healthcare data management and healthcare technology can play a major role in this regard, and those organizations that proactively use healthcare technology to detect ADEs will be a step ahead in preventing them. Some ways in which healthcare data management and healthcare technology can be used in ADE detection and prevention include:

• Electronic exchange of health information, such as laboratory results and care (e.g., discharge) summaries. This can help to improve communication among the care team as a patient passes from one team to the next.
• Interoperability between laboratory and pharmacy systems to help prevent medication errors and medication adverse effects.
• Utilize electronic health records (EHRs) and patient engagement tools to provide patient-specific data that can inform appropriate clinical decisions by providers. EHRs and patient engagement tools can also provide clinical reminders and templates to prompt and facilitate recommended clinical practices. This could result in improvements in assessment, documentation, and collaborative treatment planning for patient risk factors and aberrant behaviors.
• Leverage EHR Meaningful Use requirements by incorporating Quality Measures specific to the three types of ADEs identified in the ADE Action Plan.

ADEs can also be prevented by improving patient compliance with their medication regimen. Incorporating remote monitoring solutions such as automated medication dispensers with reminders can improve patient compliance and also prevent potential overdoses especially in seniors with memory problems.

Patient safety is a priority for all healthcare organizations and medication adverse effects are a serious threat to this. By proactively incorporating healthcare technology to detect and avoid ADEs, organizations can increasingly prevent them and improve patient safety. Acuma Health can show you how to utilize technology for proactive prevention of medication adverse effects and improved patient safety.