April 4, 2024

AI in Healthcare: Revolutionizing Patient Care and Operations

Artificial intelligence could represent the most significant breakthrough in the medical field of the last century.

3 min read

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Paul Estes

For 20 years, Paul struggled to balance his home life with fast-moving leadership roles at Dell, Amazon, and Microsoft, where he led a team of progressive HR, procurement, and legal trailblazers to launch Microsoft’s Gig Economy freelance program

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  • AI can enhance diagnostic accuracy in medical patients, decreasing mortality in diseases like pneumonia by 36%.

  • ML tools can revolutionize disease treatment by accelerating new drug research and development cycles and potentially creating personalized medicine for patient groups.

  • AI-enhanced healthcare administrative applications can reduce manual tasks by 31% and save 1000s hours of work per hospital per year.

Staff writer

From AI to FinOps, our team's collective brainpower fuels this blog.

Artificial intelligence could represent the most significant breakthrough in the medical field of the last century. 

AI tools are making waves in the healthcare industry, enhancing diagnostic accuracy, optimizing patient care, and streamlining administrative operations. The healthcare AI market boasts an impressive 40.2% CAGR. Forecasts indicate that between 2021 and 2030, the total value of AI in healthcare will surge from $11.06 billion to $187.95 billion.

The striking growth trajectory of AI in healthcare underscores its potential to revolutionize numerous aspects of medical practice. AI and machine learning technologies are poised to reshape the landscape of healthcare delivery. AI can transform areas like advancing medical imaging, facilitating personalized medicine, early detection, expediting drug discovery, and enhancing telehealth services. 

In light of these transformative impacts, the exponential rise in market value emphasizes the profound significance of AI in shaping the future of medicine.

Enhancing Diagnostic Accuracy with AI

Machine learning models are crucial in revolutionizing diagnostic accuracy, particularly within imaging and pathology. These models can efficiently process vast amounts of data for analysis, swiftly and accurately capturing relevant information. By relying on this enhanced stream of precise data, medical professionals can diagnose and treat patients more effectively.

In late 2020, Intermountain Health, a US-based not-for-profit healthcare system, integrated AI capabilities into its ePneumonia app. The updated application underwent testing in Murray, Utah, where healthcare professionals utilized ML-enhanced diagnostics to detect pneumonia in patients.

Intermountain Health, a US-based not-for-profit healthcare system, updated its ePneumonia app with AI capabilities in late 2020. The new application was tested in Murray, Utah, with healthcare professionals using ML-enhanced diagnostics to identify pneumonia in patients.

Results from Intermountain Health showcased a remarkable 36% relative decrease in 30-day mortality among pneumonia patients. The advanced data-processing capabilities of the AI app enabled it to identify and present critical information for clinicians, thereby streamlining the diagnostic process.

In medical scenarios where time sensitivity is paramount, AI-driven diagnostic tools have the potential to significantly expedite disease detection, leading to earlier identification and swift access to treatment.

Takeda's AI Model Transforms Patient Care in Oncology

Developing new medicine entails significant costs and time commitments. The average time to bring a new drug to market ranges from 5 to 10 years, with expenses often exceeding $3 billion USD. However, AI holds the potential to dramatically reduce these figures, slashing development times and streamlining the medical research and development process.

Hong Kong-based medical company Insilico provides a compelling example of AI's impact. They have a drug currently undergoing late-stage human trials, and what distinguishes their approach is the remarkably short timeframe of less than 18 months and minimal cost of $2.6 million, all thanks to their AI-driven discovery methods. Insilico utilized AI to discover, design, and initiate the testing process for new molecules.

Source - Insilico Novel AI drug discovery timeline.

Other medical firms are pushing the boundaries even further, envisioning a future where personalized drugs for small population subsets become the norm. Takeda Oncology, for instance, has developed an ML solution that collects and analyzes data on nuanced patient groups to determine the most appropriate next steps in their treatment plans.

Commenting on their AI-enhanced patient care pathway, Takeda Oncology’s head of business insights, Mayank Misra, states:

“The model we’ve built can predict the next step in the patient's journey with a certain likelihood. It’s designed to identify relevance and timing for both the patient and the physician.” 

As these ML models continue to evolve, they have the potential to revolutionize disease treatment and introduce novel patient care methods.

An AI-enabled approach to medicine heralds a new era of personalized and accessible healthcare. Research from September 2023 suggests that AI-enabled telehealth models could establish healthcare connections in underserved areas, thereby helping to bridge the healthcare gap further.

AI-Driven Solutions for Operational Streamlining

In addition to its role in diagnostics and medical care improvement, AI is crucial in streamlining healthcare operations by automating administrative tasks such as data entry, record maintenance, and appointment scheduling. This automation enables medical administrative teams to focus on higher-value tasks like patient communication, resource allocation, and strategic planning.

In late 2023, AthenaHealth introduced the athenaOne mobile app, an AI-enhanced application tailored for medical staff. This innovative app allows staff to upload images of patients' insurance cards. Leveraging ML techniques and advanced optical character recognition, the app can complete patient insurance eligibility forms, thereby reducing the administrative burden of insurance-related tasks by 31%.

In just one year, this AI-enhanced functionality saved administrators over 6,500 hours. Notably, the AI capabilities of the app extend beyond insurance tasks; they include: 

  • Drafting personalized responses to patient emails.
  • Summarizing lengthy medical documents.
  • Alerting patients when data is missing from their forms. 
  • Simplifying medical language to present patients with a clear medical plan.

Integrating AI-first medical administrative systems across the board promises enhanced operational efficiencies and streamlined hospital management. For hospitals, this translates to optimized staffing, improved resource allocation, and more time dedicated to patient care.


Integrating artificial intelligence into the medical field marks a significant breakthrough, promising improved patient outcomes, enhanced operational efficiency, and substantial cost savings. This transformative technology holds immense potential to revolutionize healthcare delivery and elevate standards of care. Early case studies demonstrate overwhelmingly positive outcomes, paving the way for further advancements and adoption. 

However, due to the private nature of medical records, medical businesses implementing AI solutions must navigate complex ethical considerations regarding patient privacy and data security. Ensuring the confidentiality and integrity of sensitive medical information is paramount to maintaining trust between patients and healthcare providers. 

Issues concerning informed consent, accountability, and transparency in AI decision-making require careful attention to uphold ethical standards and regulatory compliance. 

By leveraging AI technology responsibly and ethically, medical professionals can fully harness its potential to address healthcare challenges and deliver better patient outcomes worldwide effectively. While the journey towards fully integrating AI into medicine has just begun, the promise of improved healthcare delivery and patient care underscores its vital importance.

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