A New Era of Care (Part 1)

Healthcare systems worldwide are grappling with several critical challenges:

1. Information Overload: The rapid expansion of medical knowledge and patient data can be overwhelming. Over one million medical papers are published annually, alongside hundreds of weekly clinical trial reports. Additionally, healthcare professionals (HCPs) have more patient data than ever to take into consideration when making diagnostic & treatment decisions.
2. HCP Shortages: The global shortage of HCPs creates a strain on healthcare systems. Rising administrative burdens, including documentation and billing tasks, divert time and focus from direct patient care, further compounding inefficiencies and resulting in HCP burnout.
3. Fragmented Patient Journeys: Patients experience gaps in care coordination, leading to delays, misdiagnoses, and suboptimal treatment outcomes. This is often the result of multiple providers operating in silos with limited communication and incomplete data sharing across settings of care.

Clinical Decision Support (CDS) Tools offer transformative solutions to address these challenges. CDS algorithms assist HCPs in making informed decisions about patient care. These algorithms analyse data from various sources, such as electronic medical records (EMR), patient data and medical literature, to provide actionable insights, recommendations, or alerts in real-time.

CDS algorithms can be broadly categorized into two groups:

• Operational Efficiency Algorithms: These optimize workflows, resource allocation, and administrative processes. Examples include automated appointment scheduling, predictive staffing models, bed management systems, and real-time resource optimization.
• Clinical Algorithms: These provide HCPs with insights and recommendations for patient management, such as screening, diagnosis, treatment, and ongoing care. Examples range from basic risk calculators to AI-driven predictive models.

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1. Addressing Information Overload: CDS algorithms synthesize vast amounts of data in real-time, helping HCPs make evidence-based decisions. For example, natural language processing (NLP) algorithms, can scan and interpret medical papers, extracting key insights for clinician use. These tools also integrate diverse patient data - genomic, clinical, behavioural - to provide tailored diagnostic and treatment options, ensuring that no critical detail is overlooked, and HCPs don’t suffer from information overload.
2. Mitigating Healthcare Professional Shortages: By automating routine tasks like documentation, scheduling, and billing, CDS systems reduce the administrative burden on HCPs, freeing up time for direct patient care.
3. Streamlining Fragmented Patient Journeys: CDS algorithms enhance care coordination by integrating data from multiple providers and systems into a unified view, enabling seamless communication across care teams.

By addressing these challenges, CDS algorithms have the potential to revolutionize healthcare delivery and achieve the dual goals of personalization and prevention.

Enabling Personalized Healthcare: CDS will be a critical lever for delivering on the industry´s vision for personalized healthcare. CDS tailors care by analysing the most up-to-date clinical data and a patient’s unique characteristics to recommend treatments with the highest efficacy and lowest risk of side effects.

• Example [1]: CURATE.AI is an AI tool that supports clinicians in optimizing chemotherapy doses for individual patients. Unlike traditional toxicity-guided fixed dosing, CURATE.AI uses efficacy-driven recommendations tailored to different stages of a patient’s treatment cycle. A pilot trial showed a 20% average reduction in chemotherapy doses, with clinicians accepting 96.7% of the tool’s recommendations. This personalized approach helps minimize toxic side effects and prevent long term complications associated with chemo-enhancing quality of life, while maintaining efficacy.

Shifting to Preventative Care: Healthcare has long been criticized for its reactive, symptomatic approach to patient care. CDS tools can shift the paradigm from symptomatic to preventative care by predicting the likelihood of developing a disease based on patient-specific factors. Early detection and intervention become possible.

• Example [2]: An AI Cardiac Solution that automatically measures calcified plaque in coronary arteries using routine CT scans. This tool provides calcium scores and categorization to detect coronary artery disease (CAD) early. At Corewell Health, Nanox.AI enabled the identification of nearly 4,000 new CAD patients in 2023, compared to just 268 cases reported over the previous two years. These patients were diagnosed earlier in their CAD journey, often before symptoms emerged, leading to timely interventions and improved outcomes.

[1] NUS AI platform enables doctors to optimise personalised chemotherapy dose

[2] AI Cardiac Solution – Nanox

Let´s use a real example to make the transformational potential of CDS more tangible!

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Understanding NASH

Nonalcoholic Steatohepatitis (NASH) is a progressive liver disease marked by fat accumulation, inflammation, and fibrosis. It significantly increases the risk of cirrhosis, liver failure, and liver cancer. Despite decades of R&D efforts and substantial investment by the pharmaceutical industry, there remains significant unmet need in the disease. However, we are on the brink of a new era of hope for patients, with multiple promising late-stage therapies anticipated to reach the market soon.

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Current NASH Patient Journey

Today, the journey of a NASH patient is fraught with pain points & challenges

(1) Screening

• Asymptomatic Nature: NASH is often referred to as a "silent killer"; its asymptomatic presentation results in delayed diagnosis.
• Limited Awareness: Primary Care Physicians (PCPs) have limited disease knowledge, contributing to low screening rates in at-risk populations.

(2) Diagnosis

• Referral Bottlenecks: Delays in referrals occur due to administrative hurdles or specialist capacity constraints.
• Invasive Procedures: Diagnosis commonly relies on liver biopsy, which is costly, painful, and carries procedural risks. While non-invasive tools are emerging, they are not yet universally standardized or adopted.

(3) Treatment

• Limited Tx Options: With only one FDA-approved therapy, disease management depends heavily on lifestyle modification. The treatment landscape is evolving but remains complex, with a crowded pipeline of investigational therapies.
• Specialist Involvement: Management requires coordination across multiple specialists. Poor communication and fragmented data sharing between providers can result in suboptimal decision-making.

(4) Monitoring

• Tracking Progression: Monitoring fibrosis progression (F1–F4) is challenging due to the absence of reliable tools.
• Adherence Challenges: Patients often struggle to sustain lifestyle changes, reducing long-term effectiveness of management.
• Diverse Approaches: Lack of a universally adopted care pathway or standardized protocols leads to variability in patient management.

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Reimagining the NASH Patient Journey

Implementation of CDS use cases can revolutionize the management of NASH patients

(1) Screening

• Risk Stratification: CDS tool analyses patient data from EMR to identify high-risk individuals (e.g. obesity, type 2 diabetes, metabolic syndrome) for further evaluation during routine PCP visits
• Guideline-Integrated Alerts: CDS embedded in EMR provides PCPs with real-time prompts based on patient demographics and clinical history to recommend non-invasive screening tests (e.g. FibroScan, ELF). Alerts can include tailored education materials for PCPs to improve awareness of NASH screening guideline

(2) Diagnosis

• Referral Management: CDS prioritizes patients with advanced disease for specialist consultations, reducing bottlenecks and administrative delays. The tool automatically schedules patients flagged as higher-risk for expedited review
• Non-Invasive Diagnosis: CDS predictive algorithms integrate with non-invasive imaging tools (e.g. FibroScan), or non-invasive biomarker tests (e.g ELF), or blood tests to provide accurate, risk-based assessments, reducing the need for invasive biopsies

(3) Treatment

• Trial Matching: CDS tool matches patients to clinical trials, where they can access novel treatment by analysing data from EMRs, lab results, and imaging reports to identify individuals who meet clinical trial inclusion criteria, such as fibrosis stage, biomarkers, or comorbid conditions
• Treatment Matching: CDS algorithm leverage molecular, clinical, and lifestyle data to recommend optimal treatments and dosing based on future treatment paradigm
• Integrated Care Coordination: A centralized CDS platform synchronizes data across hepatologists, endocrinologists, dietitians, and PCPs, ensuring aligned decision-making and communication

(4) Monitoring

• Disease Progression Predication: CDS models predict likely disease trajectories based on patient-specific data, enabling pre-emptive management of complications or progression across fibrosis stages
• Remote Monitoring: CDS tools paired with wearable devices and mobile apps collect real-time data (e.g., weight, activity, liver function biomarkers), alerting providers to changes that may influence progression
• Dynamic Follow-Up: CDS systems generate personalized follow-up schedules based on disease stage and treatment response, ensuring timely check-ins and interventions

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CDS will enable a paradigm shift in healthcare delivery and add tremendous value to the system. Health Technology, Pharmaceutical, Medical Technology, and Diagnostics companies are heavily investing in CDS development as a strategic priority. Their motivations include improving care delivery, optimizing patient management, and gaining a competitive edge in the rapidly evolving healthcare landscape.

While CDS tools are often monetized directly as standalone solutions, they also create significant value for Pharma and Medtech companies. Patient identification, treatment matching and treatment monitoring use cases help get more patients on treatment, faster and ensure optimal therapeutic outcomes. Its important to consider how CDS maps to your company´s strategy and the specific leverage points you are looking to address along the patient journey.