Machine learning in healthcare:

What does it mean for your practice? 

A Q&A with healthcare tech expert, Dan Lodder

Senior Director, Business Operations

Click each card to reveal the answers:

What is machine learning in healthcare? How does it differ from AI (Artificial Intelligence)?

What are the top benefits of introducing machine learning into a specialty practice? 

What are some of the challenges of using machine learning

in healthcare? 

What machine learning revenue cycle solutions are offered by McKesson for specialty practices?

How does machine learning impact the patient’s experience?

In your opinion, what is the future of data management and how will it impact the way specialty practices operate? 

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What is machine learning in healthcare? How does it differ from AI (Artificial Intelligence)?

What are the top benefits of introducing machine learning into a specialty practice? 

What are some of the challenges of using machine learning

What machine learning revenue cycle solutions are offered by McKesson for specialty practices?

How does machine learning impact the patient’s experience?

In your opinion, what is the future of data management and how will it impact the way specialty practices operate? 

Click each card to reveal the answers:

A Q&A with healthcare tech expert, Dan Lodder

Machine learning in healthcare:

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Artificial Intelligence (AI) encompasses a wide array of technologies that enable machines or computers to execute tasks that traditionally require human intelligence. These tasks span a diverse range, including problem solving, comprehending natural language, recognizing patterns, and decision making.

What is machine learning in healthcare? How does it differ from AI (Artificial Intelligence)?

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Machine learning supports personalized medicine by facilitating the identification of optimal clinical testing and treatments for individual patients. This method considers each patient’s distinct genetic factors and medical history, recommending more efficacious and customized treatment plans. This is particularly relevant in cancer treatment, where the development of therapies increasingly relies on specific patient biomarkers. As treatment modalities continue to diversify and become more targeted, the capacity of machine learning to assess a broad spectrum of patient clinical factors is likely to usher in enhanced treatment options and outcomes.

Advancements in Personalized Medicine

Machine learning can help predict clinical, operational, and financial outcomes by sifting through both historical and real-time data. This capability is essential for advancing proactive patient care, boosting operational efficiency in practices, and enhancing overall financial performance. By analyzing clinical data, we can select the best treatment plan based on the patient’s clinical profile, leading to better outcomes. For revenue cycle management, machine learning provides valuable insights into claim evaluations, helping circumvent issues such as claim denials, underbilling, and underpayments.

Predictive Analytics

Healthcare providers generate enormous amounts of data. Leveraging machine learning can streamline the organization, management, and interpretation of this data, enhancing accessibility and usability for medical professionals. A sizable portion of clinical data in healthcare is unstructured. Utilizing AI techniques such as natural language processing and advanced large language models is key in analyzing and structuring the data. Once data is structured, machine learning methods can be applied to predict critical events, including potential patient hospitalizations, helping to improve patient care and outcomes.

Data Management

What are the top benefits of introducing machine learning into a specialty practice?

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The utility of healthcare data has historically been a challenging issue. The effectiveness of machine learning algorithms is intrinsically tied to the quality of the data they are trained on. For predictive machine learning models to function optimally, especially in supervised learning, data must be well structured and accurately labeled. Although some clinical data in electronic health records (EHRs) is already structured, a substantial portion resides in physician notes or documents from external sources. Implementing solutions like natural language processing and advanced large language models is crucial for converting this diverse data into a structured and usable format.

A common scenario in healthcare involves a patient consulting multiple physicians, each utilizing different EHR systems. This fragmentation often results in patient data being scattered across various platforms, hindering the continuity of patient information between specialists. Enhancing data exchange between EHR systems is important for addressing this challenge. However, significant hurdles remain in efficiently connecting these systems and ensuring appropriate access to consolidated patient data. As methods improve, we will start to see more continuity in patient health records, allowing better support as their care becomes more complex.

Amidst concerns that AI might supplant human healthcare workers and potentially introduce risks due to errors, it is important to clarify the role of machine learning. Machine learning is a tool meant to complement and augment the capabilities of healthcare professionals, rather than replace them. It excels in processing and analyzing large volumes of data more rapidly than human capability. However, it does not possess the nuanced understanding, empathy, and ethical judgment inherent to human practitioners. Machine learning serves as an assistant, augmenting the human touch in healthcare with data-driven insights.

Machine Learning as a Healthcare Co-Pilot

What are some of the challenges of using machine learning in healthcare?

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What machine learning revenue cycle solutions are offered by McKesson for specialty practices?

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Revolutionizing Patient Experiences with Advanced Technical Solutions

For many patients, the unknown cost of treatment can cause financial anxiety, adverse health outcomes, and ultimately strain the provider-patient relationship. Integrating machine learning into the revenue cycle management of a practice not only helps improve financial outcomes for the practice, but it also helps alleviate the financial burden for patients. For example, Glide uses machine learning to improve claims acceptance rates the first time, which ensures practices are paid in full — reducing patient stress over the financial unknowns of treatment.

Easing the Financial Burden for Patients

How does machine learning

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Looking ahead, the way data is stored and utilized in healthcare practices will become more efficient and effective. This evolution in data management is crucial for harnessing the full potential of the vast amounts of information generated in healthcare settings. With more sophisticated extraction, interpretation, and storage solutions, this data will be critical in predicting key events that can significantly enhance the quality of patient care provided by physicians.

In your opinion, what is the future of data management and how will it impact the way specialty practices operate? 

Enhancing Data Quality in Healthcare

Improving Data Accessibility Across Healthcare Systems

Enhancing patient outcomes and experiences in healthcare increasingly depends on advanced technical solutions. A prime example of this is the utilization of genomic testing in cancer treatment. This process begins with an automated alert to the healthcare provider, indicating the availability of genomic testing for a patient, based on specific clinical factors. Following this, the provider can submit an electronic order for the required test to the appropriate genomic laboratory.

Glide is a machine learning platform helping oncology and multi-specialty providers predict and reduce denials, identify payer underpayments and capture missed and underbilled charges. Glide analyzes data to predict and prevent negative events in the revenue cycle. Glide identifies issues prior to claim submission and in the revenue cycle workflow. The predictive, prospective, and integrated features in Glide are unique and differ from existing analytic solutions where information is reactive, retrospective, and not integrated with the workflow.