Machine learning and AI are taking healthcare by storm. Are you ready to rumble?

Fueled by healthcare’s data deluge, enthusiasm for machine learning and artificial intelligence is on the rise as providers recognize the need for automated and analytic tools for managing patients more effectively.

According to a recent report, artificial intelligence in healthcare is projected to reach $34 billion by 2025 as the need for systems to extract, analyze and organize troves of patient data grows.

Harnessing the power of AI and machine learning has become a distinct, competitive advantage for data-driven healthcare organizations looking to piece together fragmented, often disconnected data sources, for meaningful insights across the enterprise. Machine learning algorithms used to understand, reason and learn can help hospitals and health systems detect patterns and normalize data to gain a complete and accurate picture of a patient’s health, map care pathways and processes, reduce costs in care, and improve outcomes.

Yet, the task of implementing machine learning projects comes with challenges.

Poor data quality continues to undermine treatment, outcomes and costs. Good analysis won’t result from bad data, but because clinical documents and medical images are far too large for the human mind to compute, machine learning projects are becoming more frequent, especially as population health and value-based care initiatives become increasingly critical.

As the momentum to adopt machine learning ramps up, healthcare organizations will need to design a plan that takes advantage of the insights they gain as they look to customize treatments, improve diagnosis decision making, and forecast the spread of infections. These goals will be key drivers of their operations especially against the background of their need to produce quality care metrics that enables them to be paid under value-based care payment programs.

Building Analytics through Partnerships
To conduct a successful machine learning project many health organizations are turning to third party managed service providers that already have experience handling large volumes of patient data sets and can provide valuable offerings such as patient matching tools and cybersecurity technology. Engaging a managed service provider (MSP) that has a partnership with a reputable HIPAA compliant cloud vendor can help the healthcare organization build, train, and host their machine learning models at scale. Additionally, customized solutions with on-board computing power capable of running real time deep learning inference on sophisticated models helps deliver the performance, efficiency and responsiveness healthcare organizations want to see.

At the heart of machine learning and AI, is the ability to perform pattern recognition, probability theory, optimization and statistics. Machine learning algorithms can be trained to learn from the data, build a model to recognize common patterns, devise data-driven predictions and uncover insights that contribute to informed decisions.

One example of how machine learning can be applied in healthcare is the case of performing demographic matching of data for an Enterprise Master Patient Index (EMPI)—a centralized database containing patient medical records across various departments and geographic locations. Patients are assigned a unique identifier in the EMPI, but data that comes from multiple sources can have data input errors, name variances, duplications and other precarious inaccuracies.

Unlike traditional algorithms, machine learning algorithms are able to adjust themselves based on the feedback provided by human intervention. In the case of the EMPI and its primary goal of demographic matching, the training process for machine learning hinges on manual remediation typically performed by health information management (HIM) professionals responsible for reviewing and linking duplicate records together under a single identifier.

This manual intervention tends to occur in cases where there is ambiguity between two or more records, and the action performed represents an enormous amount of information that traditional algorithms simply discard. The challenge in using this kind of information is in the sheer number of human interactions required for an algorithm of this type to truly outperform human remediation. This is because the system must be able to detect broad patterns where users consistently take an action of marking a pair of records unique or as a match. Training, however, is greatly simplified in a cloud environment where usage statistics across many implementations can be gathered to produce a highly intelligent record resolution algorithm, thereby reducing manual duplicate resolution tasks and diminishing false-positive/false-negative errors. Data centralization in the cloud is also cost effective because resources can be dynamically allocated to multiple customers on demand.

The Business Advantage of Machine Learning
While healthcare organizations must identify, collect and normalize the data prior to a machine learning project, they’ll have to keep in mind their efforts will be used toward the greater goal of meeting specific performance metrics under their health insurers’ payment programs. For example, under the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA), there are quality payment programs such as the Advanced Alternative Payment models (APMs) and the Merit-based Incentive Payment System (MIPS). These initiatives require that health providers receive payments based on their performance and improved patient outcomes.

Other trends are impacting health data analytics too. Population health management programs, which involve treating and monitoring groups of patients with specific medical conditions such as diabetes, hypertension, or cancer, are increasingly being implemented.

Additionally, data that incorporates social determinants of health such as genetics, behavior and environment (housing, education, transportation, income, and food insecurity) are important health related data that needs to be included when analyzing the well-being of an individual, a group, or wider population.

As healthcare organizations embark on a machine learning project, they’ll have to ask themselves the following questions:
• What are the best use cases for a machine learning project, and where can the organization reap the best return on investment from the project?
• Does the organization have the right talent, data and technology to execute machine learning opportunities?
• How can the organization build trust and transparency into machine learning platforms and applications?

As more and more healthcare organizations step out of the traditional boundaries of their legacy infrastructure and embrace cloud and machine learning to overcome the complexity of their disparate, expanding IT environment, the more they can learn from their data, yielding greater quality insights. However, those embarking on machine learning projects must remember that their success will hinge on highly-skilled resources and how well their organization deploys these insights into actionable measures that improve outcomes, reduce costs and raise the quality of care.

Dan Cidon is CTO and co-founder NextGate, a global leader in healthcare identity management.

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