Many of today’s headlines are talking about how machine learning and artificial intelligence are transforming industry. The healthcare sector is one of the major industries affected by this trend, in large part because organizations are recognizing the need for improved data processing and analysis, according to research and healthcare publications such as Health Network, ITHealthcareNews, and Healthcare Data Management. 

Slow Path to Digitization 

Analysts project that by 2018, 30 percent of providers will run cognitive analytics on patient data. For instance, IDC has said that healthcare will access cognitive solutions for close to 50 percent of cancer patients, resulting in reduced costs and mortality rates.

Unfortunately, healthcare has been slow to digitize its information on patients, and it has been a struggle to use the overwhelming amount of data it collects. New software, however, is making it possible to analyze large sets of patient data and apply relationship modeling in a predictive way and in real time—improving overall operating efficiency and reducing unnecessary costs. 

Enter Machine Learning and AI 

Machine learning (ML) and artificial intelligence (AI) is presenting a major evolution in the advancement of healthcare. Smart institutions using ML and AI approaches to analyze health record data are redefining drug discovery, assisting and automating diagnoses, and predicting and preventing diseases. 

For instance, tomography data in patients’ digital records can be analyzed to see whether they are most at risk for Disease A or Disease B. AI algorithms can predict post-discharge outcomes, reducing readmissions and optimizing patient flow. In other words, physicians can use these new biotech and IT solutions to make medical diagnostics faster, more precise, and more manageable.  

In another example, AiCure uses mobile technology and facial recognition to capture patient data. Automated algorithms then identify patients and the medications being administered. That data is transmitted in real-time back to a clinician through a HIPAA-compliant network. Clinicians can confirm that the patients are taking their medication as directed or red flag adverse events.

Meanwhile, leaders in healthcare institutions can use machine learning and AI tech to better analyze data and understand the daily operations of their organizations. For instance, data mining and Big Data analytics can be used to measure service quality. Measuring the quality of health care is important because it tells leaders not only how the health system is performing, but also can lead to new training or talent development practices that will improve overall patient care.  

Moving Forward 

How will medicine adapt to this new tech? How will healthcare leaders use data to improve the management of healthcare organizations? What will be the long-term impact on patients’ health and autonomy?  

To advance machine learning and AI approaches even further, industry leaders will need to understand and address not only the potential benefits and uses of this new technology, but also the limitations and barriers. To that end, today’s healthcare researchers will need to seek more data on a plethora of issues, including: 

• predicting individual patient outcomes

• patient risk stratification

• biomarker discovery

• learning from sparse/missing/imbalanced data

• medical imaging

• clustering and phenotype discover

• feature selection/dimensionality reduction

• exploiting and generating ontologies

• text classification and mining biomedical literature

• mining, processing, and making sense of clinical notes

• parsing biomedical literature

• brain-imaging technologies and related models

• time series analysis with medical applications

• efficient, scalable processing of clinical data

• methods for vitals monitoring

• machine learning systems that assist with evidence-based medicine

• integration of clinical, omics, social media, and mobile sources

• public health and pharmaco-surveillance.

Role of Talent Development  

All of this begs the question: How does machine learning, AI, and other Big Data technologies influence the learning and talent development community in healthcare? 

Most leaders are familiar with reviewing large amounts of data, but without actual meaning behind those facts and figures. With the advent of AI and machine learning, data can directly improve services and teach organizational leaders to look for gaps and discrepancies, as well as positive results and practices. For example, with regards to care, new tech can analyze data on such matters as the service support environment of a hospital, patient wait times, the amount of time a physician spends with a patient, and the number of services used by patients. Internally, ML and AI tech can be used to look at the number of employees who use organizational benefits, employee satisfaction and engagement, and how well the employee is retaining organizational training.  

Fortunately, talent development and L&D professionals are familiar with analyzing data to develop future scenarios and solutions. They can help healthcare leaders on this journey to using new tech to analyze and apply data efficiently and effectively to improve healthcare delivery and the patient experience, help patients adhere to a plan of care, and reduce institutional costs.