Machine Learning System Catches Two-Thirds More Prescription Medication Errors than Existing Clinical Decision Support Systems at Two Major Hospitals

Researchers find a savings of more than one million dollars and prevention of hundreds, if not thousands, of adverse drug events could have been had with machine learning system

Support for artificial intelligence (AI) and machine learning (ML) in healthcare has been mixed among anatomic pathologists and clinical laboratory leaders. Nevertheless, there’s increasing evidence that diagnostic systems based on AI and ML can be as accurate or more accurate at detecting disease than systems without them.

Dark Daily has covered the development of artificial intelligence and machine learning systems and their ability to accurately detect disease in many e-briefings over the years. Now, a recent study conducted at Brigham and Women’s Hospital (BWH) and Massachusetts General Hospital (MGH) suggests machine learning can be more accurate than existing clinical decision support (CDS) systems at detecting prescription medication errors as well.

The researchers published their findings in the Joint Commission Journal on Quality and Patient Safety, titled, “Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors: A Clinical and Cost Analysis Evaluation.”

A Retrospective Study

The study was partially retrospective in that the researchers compiled past alerts generated by the CDS systems at BWH and MGH between 2009-2011 and added them to alerts generated during the active part of the study, which took place from January 1, 2012 to December 31, 2013, for a total of five years’ worth of CDS alerts.

They then sent the same patient-encounter data that generated those CDS alerts to a machine learning platform called MedAware, an AI-enabled software system developed in Ra’anana, Israel.

MedAware was created for the “identification and prevention of prescription errors and adverse drug effects,” notes the study, which goes on to state, “This system identifies medication issues based on machine learning using a set of algorithms with different complexity levels, ranging from statistical analysis to deep learning with neural networks. Different algorithms are used for different types of medication errors. The data elements used by the algorithms include demographics, encounters, lab test results, vital signs, medications, diagnosis, and procedures.”

The researchers then compared the alerts produced by MedAware to the existing CDS alerts from that 5-year period. The results were astonishing.

According to the study:

  • “68.2% of the alerts generated were unique to the MedAware system and not generated by the institutions’ CDS alerting system.
  • “Clinical outlier alerts were the type least likely to be generated by the institutions’ CDS—99.2% of these alerts were unique to the MedAware system.
  • “The largest overlap was with dosage alerts, with only 10.6% unique to the MedAware system.
  • “68% of the time-dependent alerts were unique to the MedAware system.”

Perhaps even more important was the results of the cost analysis, which found:

  • “The average cost of an adverse event potentially prevented by an alert was $60.67 (range: $5.95–$115.40).
  • “The average adverse event cost per type of alert varied from $14.58 (range: $2.99–$26.18) for dosage outliers to $19.14 (range: $1.86–$36.41) for clinical outliers and $66.47 (range: $6.47–$126.47) for time-dependent alerts.”

The researchers concluded that, “Potential savings of $60.67 per alert was mainly derived from the prevention of ADEs [adverse drug events]. The prevention of ADEs could result in savings of $60.63 per alert, representing 99.93% of the total potential savings. Potential savings related to averted calls between pharmacists and clinicians could save an average of $0.047 per alert, representing 0.08% of the total potential savings.

“Extrapolating the results of the analysis to the 747,985 BWH and MGH patients who had at least one outpatient encounter during the two-year study period from 2012 to 2013, the alerts that would have been fired over five years of their clinical care by the machine learning medication errors identification system could have resulted in potential savings of $1,294,457.”

Savings of more than one million dollars plus the prevention of potential patient harm or deaths caused by thousands of adverse drug events is a strong argument for machine learning platforms in diagnostics and prescription drug monitoring.

“There’s huge promise for machine learning in healthcare. If clinicians use the technology on the front lines, it could lead to improved clinical decision support and new information at the point of care,” said Raj Ratwani, PhD (above), Vice President of Scientific Affairs at MedStar Health Research Institute (MHRI), Director of MedStar Health’s National Center for Human Factors in Healthcare, and Associate Professor of Emergency Medicine at Georgetown University School of Medicine, told HealthITAnalytics. [Photo copyright: MedStar Institute for Innovation.)

Researchers Say Current Clinical Decision Support Systems are Limited

Machine learning is not the same as artificial intelligence. ML is a “discipline of AI” which aims for “enhancing accuracy,” while AI’s objective is “increasing probability of success,” explained Tech Differences.

Healthcare needs the help. Prescription medication errors cause patient harm or deaths that cost more than $20 billion annually, states a Joint Commission news release.

CDS alerting systems are widely used to improve patient safety and quality of care. However, the BWH-MGH researchers say the current CDS systems “have a variety of limitations.” According to the study:

  • “One limitation is that current CDS systems are rule-based and can thus identify only the medication errors that have been previously identified and programmed into their alerting logic.
  • “Further, most have high alerting rates with many false positives, resulting in alert fatigue.”

Alert fatigue leads to physician burnout, which is a big problem in large healthcare systems using multiple health information technology (HIT) systems that generate large amounts of alerts, such as: electronic health record (EHR) systems, hospital information systems (HIS), laboratory information systems (LIS), and others.

Commenting on the value of adding machine learning medication alerts software to existing CDS hospital systems, the BWH-MGH researchers wrote, “This kind of approach can complement traditional rule-based decision support, because it is likely to find additional errors that would not be identified by usual rule-based approaches.”

However, they concluded, “The true value of such alerts is highly contingent on whether and how clinicians respond to such alerts and their potential to prevent actual patient harm.”

Future research based on real-time data is needed before machine learning systems will be ready for use in clinical settings, HealthITAnalytics noted. 

However, medical laboratory leaders and pathologists will want to keep an eye on developments in machine learning and artificial intelligence that help physicians reduce medication errors and adverse drug events. Implementation of AI-ML systems in healthcare will certainly affect clinical laboratory workflows.

—Donna Marie Pocius

Related Information:

AI and Healthcare: A Giant Opportunity

Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors:  A Clinical and Cost Analysis Evaluation

Machine Learning System Accurately Identifies Medication Errors

Journal Study Evaluates Success of Automated Machine Learning System to Prevent Medication Prescribing Errors

Differences Between Machine Learning and Artificial Intelligence

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Clinical laboratories working with AI should be aware of ethical challenges being pointed out by industry experts and legal authorities

Experts are voicing concerns that using artificial intelligence (AI) in healthcare could present ethical challenges that need to be addressed. They say databases and algorithms may introduce bias into the diagnostic process, and that AI may not perform as intended, posing a potential for patient harm.

If true, the issues raised by these experts would have major implications for how clinical laboratories and anatomic pathology groups might use artificial intelligence. For that reason, medical laboratory executives and pathologists should be aware of possible drawbacks to the use of AI and machine-learning algorithms in the diagnostic process.

Is AI Underperforming?

AI’s ability to improve diagnoses, precisely target therapies, and leverage healthcare data is predicted to be a boon to precision medicine and personalized healthcare.

For example, Accenture (NYSE:ACN) says that hospitals will spend $6.6 billion on AI by 2021. This represents an annual growth rate of 40%, according to a report from the Dublin, Ireland-based consulting firm, which states, “when combined, key clinical health AI applications can potentially create $150 billion in annual savings for the United States healthcare economy by 2026.”

But are healthcare providers too quick to adopt AI?

Accenture defines AI as a “constellation of technologies from machine learning to natural language processing that allows machines to sense, comprehend, act, and learn.” However, some experts say AI is not performing as intended, and that it introduces biases in healthcare worthy of investigation.

Keith Dreyer, DO, PhD, is Chief Data Science Officer at Partners Healthcare and Vice Chairman of Radiology at Massachusetts General Hospital (MGH). At a World Medical Innovation Forum on Artificial Intelligence covered by HealthITAnalytics, he said, “There are currently no measures to indicate that a result is biased or how much it might be biased. We need to explain the dataset these answers came from, how accurate we can expect them to be, where they work, and where they don’t work. When a number comes back, what does it really mean? What’s the difference between a seven and an eight or a two?” (Photo copyright: Healthcare in Europe.)

What Goes in Limits What Comes Out

Could machine learning lead to machine decision-making that puts patients at risk? Some legal authorities say yes. Especially when computer algorithms are based on limited data sources and questionable methods, lawyers warn.

Pilar Ossorio PhD, JD, Professor of Law and Bioethics at the University of Wisconsin Law School (UW), toldHealth Data Management (HDM) that genomics databases, such as the Genome-Wide Association Studies (GWAS), house data predominantly about people of Northern European descent, and that could be a problem.

How can AI provide accurate medical insights for people when the information going into databases is limited in the first place? Ossorio pointed to lack of diversity in genomic data. “There are still large groups of people for whom we have almost no genomic data. This is another way in which the datasets that you might use to train your algorithms are going to exclude certain groups of people altogether,” she told HDM.

She also sounded the alarm about making decisions about women’s health when data driving them are based on studies where women have been “under-treated compared with men.”

“This leads to poor treatment, and that’s going to be reflected in essentially all healthcare data that people are using when they train their algorithms,” Ossorio said during a Machine Learning for Healthcare (MLHC) conference covered by HDM.

How Bias Happens 

Bias can enter healthcare data in three forms: by humans, by design, and in its usage. That’s according to David Magnus, PhD, Director of the Stanford Center for Biomedical Ethics (SCBE) and Senior Author of a paper published in the New England Journal of Medicine (NEJM) titled, “Implementing Machine Learning in Health Care—Addressing Ethical Challenges.”

The paper’s authors wrote, “Physician-researchers are predicting that familiarity with machine-learning tools for analyzing big data will be a fundamental requirement for the next generation of physicians and that algorithms might soon rival or replace physicians in fields that involve close scrutiny of images, such as radiology and anatomical pathology.”

In a news release, Magnus said, “You can easily imagine that the algorithms being built into the healthcare system might be reflective of different, conflicting interests. What if the algorithm is designed around the goal of making money? What if different treatment decisions about patients are made depending on insurance status or their ability to pay?”

In addition to the possibility of algorithm bias, the authors of the NEJM paper have other concerns about AI affecting healthcare providers:

  • “Physicians must adequately understand how algorithms are created, critically assess the source of the data used to create the statistical models designed to predict outcomes, understand how the models function and guard against becoming overly dependent on them.
  • “Data gathered about patient health, diagnostics, and outcomes become part of the ‘collective knowledge’ of published literature and information collected by healthcare systems and might be used without regard for clinical experience and the human aspect of patient care.
  • “Machine-learning-based clinical guidance may introduce a third-party ‘actor’ into the physician-patient relationship, challenging the dynamics of responsibility in the relationship and the expectation of confidentiality.”    
“We need to be cautious about caring for people based on what algorithms are showing us. The one thing people can do that machines can’t do is step aside from our ideas and evaluate them critically,” said Danton Char, MD, Lead Author and Assistant Professor of Anesthesiology, Perioperative, and Pain Medicine at Stanford, in the news release. “I think society has become very breathless in looking for quick answers,” he added. (Photo copyright: Stanford Medicine.)

Acknowledge Healthcare’s Differences

Still, the Stanford researchers acknowledge that AI can benefit patients. And that healthcare leaders can learn from other industries, such as car companies, which have test driven AI. 

“Artificial intelligence will be pervasive in healthcare in a few years,” said

Nigam Shah, PhD, co-author of the NEJM paper and Associate Professor of Medicine at Stanford, in the news release. He added that healthcare leaders need to be aware of the “pitfalls” that have happened in other industries and be cognizant of data. 

“Be careful about knowing the data from which you learn,” he warned.

AI’s ultimate role in healthcare diagnostics is not yet fully known. Nevertheless, it behooves clinical laboratory leaders and anatomic pathologists who are considering using AI to address issues of quality and accuracy of the lab data they are generating. And to be aware of potential biases in the data collection process.

—Donna Marie Pocius

Related Information:

Accenture: Healthcare Artificial Intelligence

Could Artificial Intelligence Do More Harm than Good in Healthcare?

AI Machine Learning Algorithms Are Susceptible to Biased Data

Implementing Machine Learning in Healthcare—Addressing Ethical Challenges

Researchers Say Use of AI in Medicine Raises Ethical Questions

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