Healthcare is rife with complex data stored in multiple places and evolving every day. That makes it a good target for a form of artificial intelligence known as machine learning.
Oxford defines machine learning as “ the analysis of patterns and draw inferences from them, thereby enabling the use and development of computer systems capable of learning and adapting without following explicit instructions.”
In recent years , machine learning has been shown to be useful for diagnosis and to help improve the efficiency of medical coding. But there are many other places where machine learning can be used without progress. Why is this happening?
Harshith Ramesh is Co-CEO of Episource, a risk adjustment services and medical software provider group and health plans, and experts in machine learning. We caught up with him to discuss why machine learning is great for healthcare, how it can help with diagnosis and coding, and most importantly, what’s holding it back in healthcare.
Q. You think healthcare is a unique preparation for machine learning. why. Machine learning is a branch of artificial intelligence that uses data to mimic the way humans learn, improving performance on a given task over time. In the healthcare industry, the technology is used to detect patterns in patient health information and improve its algorithms to become more precise as it learns from available data.
Efficient, accurate and cost-effective as more supplier organizations take on downside risk in the coming years under a value-based contracting model Measuring patient outcomes has become more important than ever. Machine learning is a key tool that vendors can use to achieve this.
Healthcare is the only readiness for machine learning as the volume of patient data has grown exponentially over the past two decades . Today, approximately 30% of the world’s data is generated by the healthcare industry.
This is partly due to the widespread use of electronic health records, which first gained attention in the 1990s. The digitization of patient information not only increases the amount of data that exists, but also makes it easily accessible for machine learning applications.
In addition to EHR, healthcare data is also generated by a growing number of sources such as medical devices, wearables, Data clearinghouses, laboratories and supplier offices. This abundance of data is critical for machine learning models to become more accurate in predicting patient outcomes. This can help provider organizations gain a more complete picture of a patient’s health.
Healthcare data is generated in nature rather than in other industries, which makes it particularly compatible with machine learning techniques. This is due to standardized procedures, automated systems, medical coders and expert physicians – all of which help remove as much subjectivity as possible from the data.
For example, the industry has established standardized datasets that medical institutions must use, such as international disease for diagnostic information Classification (ICD-10) code or National Drug Code (NDC) for drug identification.
Regulations around how healthcare organizations store and transmit data in the EHR also make it easier for models powered by machine learning Analyze data, spot trends and apply algorithms to improve patient outcomes.
Q. How can machine learning help healthcare provider organizations in diagnosis?
A. Machine learning has a variety of applications in the clinical field. One such application is predictive modeling, a commonly used statistical technique that can be used to predict future behavior.
Through predictive modeling, providers can effectively predict whether high-risk patients will develop sepsis or other types of complications after surgery. This helps determine whether they may need to take additional precautions to reduce this risk, such as calling patients for regular check-ups or optimizing resources to target potentially high-risk patients.
It can also support population health management by creating dynamic groups that are based on a given set of health Membership is segmented by condition or other type of pattern. This knowledge can then be shared with the care management team, who then determine which interventions will have the most impact on a given cohort.
Finally, machine learning models can help providers with clinical suspicion. The technology can be used to analyze diagnostic data to predict which patients need care most urgently and identify gaps in their medical history.
Machine learning can also help providers determine whether a particular treatment will work for a patient, for example, by analyzing a patient’s entire health history , to find the safest and most effective medicines your doctor can prescribe based on your diagnosis.
Q. How can machine learning help healthcare provider organizations with medical coding?
A. The provider’s documentation process is often complete, but it is difficult to convert this data into just one of the more than 72,000 ICD-10 diagnostic codes.
As vendor organizations strive to improve data quality, they may choose to leverage and extend AI technologies to help improve the efficiency of medical coding across the risk adjustment continuum and quality – prospective, simultaneous and retrospective.
Before and during a visit, machine learning algorithms can quickly analyze patient medical information and provide providers with patient health real-time snapshots.
Clinicians can spend less time on heavy administrative tasks and more time on Provide focused and timely care to patients. What’s more, prospective coding powered by machine learning can reveal chronic conditions that were documented in the past but not at the time of visit.
Machine learning can intelligently and automatically parse the unstructured information in the EHR and identify the most accurate code. For example, it can also be used retrospectively to improve the speed and accuracy of coding, saving supplier organizations time and costs – enabling them to direct more resources where they are needed most.
This in turn helps provider groups meet quality measures, track performance and ensure regular patient assessments.
Q. What’s holding back healthcare from getting more out of machine learning progress?
A. The biggest factor causing healthcare hesitancy to adopt machine learning is the hurdles the industry faces in improving interoperability. Competition and the consequent lack of coordination between health systems has led to numerous challenges.
From inconsistent technical standards to disparate health information privacy policies, from different methods of obtaining patient consent Difficulty getting major EHRs to coordinate with each other, healthcare organizations Many hurdles must be overcome in the quest for interoperability.
This creates data gaps between different EHR applications and networks, creating silos in the data, which Silos will inform the most urgent and effective patient interventions.
Putting all of this together, the healthcare regulatory environment continues to become increasingly complex, with annual reviews of government-funded programs Rule revisions. This has raised skepticism among vendors about the ability of technologies such as machine learning to adapt to these evolving regulatory changes.
Doubt that there will always be any emerging technology, especially when providing providers with a one-size-fits-all black-box solution These solutions are not effective in enabling them to provide better patient care.
Technology vendors offering solutions that leverage machine learning techniques should be transparent about how they can improve workflow efficiency and reduce Manage the burden and give providers more time to focus on delivering care.
Vendors should act as an ongoing resource and partner throughout the implementation process and beyond, ensuring that their solutions continue to work , to better understand the membership of provider organizations and improve patient outcomes.
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