How can Healthcare Providers & Payers maximize Value & ROI from their AI enabled Use Cases using a Portfolio Management Paradigm
Context
Recently, I had the privilege of presenting at the Reuters Total Health Conference in Chicago, where the predominant theme was the utility and value of AI in healthcare. Below are some key insights from the presentations I attended and feedback from my own session on "A Portfolio Management Approach to AI in Healthcare," which was well received.
Key Takeaways from the Conference:
The discussions made it clear that "AI in Healthcare" is not well understood as a holistic concept encompassing a portfolio of technologies, modalities, platforms, tools, and capabilities. Asking ten healthcare professionals what AI involves will likely yield ten different answers, highlighting the complexity and confusion surrounding the topic.
Why Healthcare and Life Sciences need a Portfolio Management Approach to AI
?Given the onrush of AI spending in healthcare, it’s important that healthcare systems understand the different AI technologies, how they are deployed and which offer the best value and return on investment (ROI). Healthcare providers and payers, as well as pharma, medical devices and diagnostics companies are already spending fortunes on AI software.
?AI spending in healthcare and life sciences is projected to grow from $11.6 billion in 2024 to $19 billion by 2027, with a five-year CAGR of 16.6%, per Gartner. That is astronomical spend that demands an understanding of the portfolio of technologies, platforms and tools enabling this spend.
"AI in Healthcare is far more than Gen AI and LLMs, period"!
What is happening today is a very fragmented, tactical, often academic, point solutions driven approach to AI innovation siloed-ed by AI technology (Gen AI dominates given dis-proportionate VC funding and hype), department and function, core competence, build vs. buy vs. outsource considerations as well as risk aversion. There is very little evidence of these AI startup driven “solutions looking for problems” delivering value and ROI to healthcare providers and payers resulting in justifiable frustration and disappointment.
Given this scenario, healthcare and life sciences leaders should adopt a Portfolio Management approach to AI, similar to managing their tech portfolio comprising EHRs, ERP, CRM, SCM, RCM etc. which has served them well as a proven approach assuring value and ROI.
?AI is best understood as a portfolio of complementary technologies and capabilities, some of which simply automate manual and often repetitive administrative tasks while others deliver in-depth analysis, predictions and courses of action to optimize outcomes and value. The AI Portfolio would consist of all the technologies/ modalities that would deliver value and ROI for their critical use cases as illustrated in figure 1 below, and articulated in detail in my previous blogpost, ‘Why does Healthcare and Life Sciences need a Portfolio Management Approach to AI’..check it out here.
AI Tech Innovation Portfolio for Healthcare’ Adoption
Figure 1A. AI Technology Innovation Portfolio for Healthcare and Life Sciences adoption. Copyright Andy De (2024). All rights reserved.
Figure 1B. Andy De's Reference Portfolio of 16 High-Value AI Use Cases deployed in Healthcare mapping the AI modality to each Use Case articulated in detail in the post below. Copyright Andy De (2024). All rights reserved.
The AI Adoption Paradox – Eager Vendors and “Not Yet Ready for Adoption” Providers, Payers, Physicians and Nurses
?It is a sobering reality that many healthcare provider and payer executives have dis-proportionately invested their $$$, time and efforts on Gen AI relative to other modalities, with vendors offering “solutions looking for a problem” given the huge VC investments in Gen AI startups. They are frustrated because they have not seen the value and ROI on these efforts (which often include requests to train the vendor’s models that they have no time or resources for!) which is hardly surprising since Gen AI is the least mature and has not proven its efficacy for use cases beyond Ambient Clinical Intelligence (ACI) also called “Ambient Listening” at this time.
The disconnect between AI startups offering their “panacea for healthcare” and the obvious lack of readiness from providers and payers reminded me of the classic ‘New Product and Services adoption paradox’ articulated below [Ref 1].
AI vendors (especially startups vying for sustained investments) need to be aware of this adoption paradox, to avoid ‘falling into the chasm’ with models and algorithms that do not address real world challenges confronting physicians, nurses, providers and payers today….and their lack of AI adoption readiness, which is amply in evidence.
The Psychology of Patient/ Provider/ Payer Adoption and why most new AI/ Tech Products and Services fail to win Mind and Market Share
Figure 2. The Psychology of Customer Adoption from 'Eager Sellers and Stony Buyers: Understanding the Psychology of New Product Adoption' by John T. Gourville, Harvard Business Review, June 2026.
The challenges with AI adoption are well understood when viewed thru the seminal lenses of ’Eager Sellers and Stony Buyers – Understanding the Psychology of New Product Adoption’ by John T. Gourville, Harvard Business Review (HBR), June 2006, via this 2x2 matrix above. [Ref 1]
?Gourville argues that the greater the level of change in customer behavior demanded by a new product or service, the greater the barrier to customer adoption, despite the hype and ‘value promise’ offered by the new product or technology.
?Gourville further makes the point that ‘producers of innovation’ often overestimate customer adoption by a factor of 3X while consumers au contraire, allocate significant value to their current product or service that is often 3X over and above the new product or service being offered.
?The framework stratifies and predicts success potential with new product/service adoption predicated on the degree of change in customer behavior needed and their resistance to switch from their current solutions into these four quadrants:
A Strategic Portfolio Management Framework from AI powered Use Cases in Healthcare – a compelling predictor of Value and ROI
Given these challenges associated with new product and service adoption in healthcare, I have modified and adapted this strategy blueprint above to craft this?2X2 Strategy Matrix below that lends itself well to AI / tech adoption in healthcare.
The Y-Axis represents ‘Degree of change behavior needed from Patients, Providers, Physicians, Nurses, Payers and Policy Makers’ from AI Adoption, while? the X-Axis represents ‘Patient/Provider/Payer Benefits and ROI from AI adoption’.
Figure 3. A Strategic Value Based Framework for Segmenting and Managing your Portfolio of AI Use Cases to assure ROI on your AI investments for your Healthcare Organization. Copyright Andy De (2024). All rights reserved.
The?Predictive Power of this Strategic Innovation Portfolio Matrix?can be leveraged as follows:
The key premise is that the greater the change in behavior needed (e.g. "training your AI model" by physicians, nurses, providers, and payers), to deliver new products or services, the greater the barrier to customer (patients/ providers/payers) adoption, despite the promise of value delivered by the new product or technology, unless this is backed up with robust and demonstrable real world evidence of value and ROI to drive traction and adoption.
For most mainstream customers (Patients/Providers/Payers) to adopt new innovation, they demand a value proposition that is virtually 9 -10X times that is delivered by their current product or service, to justify their ‘switching costs’, that most innovators or vendors are oblivious to! This is often why over 70-80% of new technology or software products fail to ‘cross the chasm’ into the mainstream market!
This is precisely what I would predict for the plethora of Gen AI dominant offerings from startups today – 70-80% of these “we will solve Healthcare with our AI models” offerings will not make it beyond the next 1000 days and will fade into oblivion!
Let’s apply this strategic portfolio driven segmentation to high value use cases enabled by the core AI tech modalities highlighted in figure 1 above. We will deliberately focus this discussion only on the ‘Smash Hits’ and ‘Easy Sells’ in the upper two quadrants which have been or are seeing real-world adoption in healthcare and are not ‘solutions looking for problems’ by any stretch of the imagination!
A. High Value Machine Learning Use Cases deployed in Healthcare
Figure 4. Portfolio of High Value Machine Learning Use Cases in Healthcare. Copyright Andy De (2024). All rights reserved.
High-Value Use Cases Summary:
A1.?? Fraud Waste and Abuse Detection [Smash Hit]
Fraud, waste and abuse is a huge challenge in healthcare and more than $ 100 Bn (10% of the one trillion $$$ spend in healthcare or more) are lost annually in the US.
?AI for Fraud, Waste, and Abuse (FWA) in healthcare for payers?utilizes machine learning algorithms to analyze vast amounts of claims data, identifying patterns and anomalies that could indicate fraudulent activity, allowing payers to proactively detect and prevent potential issues before significant financial damage occurs, significantly improving the accuracy and speed of FWA detection compared to manual review methods. The benefits and ROI are self-evident and offers a rapid payback.
Companies offering AI solutions for payment integrity and fraud, waste and abuse detection in healthcare include Shift Technology, Codoxo, Healthcare Fraud Shield, 4L Data Intelligence, Alivia Analytics, Mastercard, IBM and SAS.
A2.?? Proactive Detection of Patient Sepsis risk to lower Mortality [Smash Hit]
Sepsis occurs when an infection triggers a chain reaction throughput the body. Inflammation can lead to blood clots and leaking blood vessels, resulting in organ damage or organ failure often leading to death. It is a sobering reality that about 1.7 MM adults in the US develop sepsis each year and more than 250,000 of them die. [Ref 2].
Dr. Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins, who lost a nephew to sepsis, has developed the Targeted Real-Time Early Warning System using machine learning to proactively detect sepsis. Combining a patient's medical history with current symptoms and lab results, the machine-learning system shows clinicians when someone is at risk for sepsis and suggests treatment protocols, such as starting antibiotics.
The AI tracks patients from when they arrive in the hospital through discharge, ensuring that critical information isn't overlooked even if staff changes or a patient moves to a different department.
In 82% of sepsis cases, the AI was accurate nearly 40% of the time. Previous attempts to use electronic tools to detect sepsis caught less than half that many cases and were accurate 2% to 5% of the time. All sepsis cases are eventually caught, but with the current standard of care, the condition kills 30% of the people who develop it.
?This use case using machine learning algorithms, if deployed widely has the potential to significantly reduce the 250,000 deaths from the nearly 2 MM patients afflicted by sepsis each year, with life-saving impact. Dr. Saria is commercializing the Targeted Real-Time Early Warning System (TREWS) thru her well-funded startup Bayesian Health.
A3.?? Surgical / OR Performance + Throughput improvement thru actionable insights delivered with Hybrid AI [Smash Hit]
Surgeons perform surgeries on patients in the O/R with life impacting procedures under conditions of stress and less than perfect data and visibility.
Using Hybrid AI comprising machine learning, gen AI, machine vision, predictive analytics and reporting, Caresyntax collects surgery data from EHRs, video, audio and medical devices. They analyze the data to provide actionable insights to support surgical teams:
o?? Preoperative:?Helps optimize surgical preparation?
o?? Intraoperative:?Provides real-time support tools?
o?? Postoperative:?Helps analyze risk assessment and personalize patient care?
Caresyntax shares insights with healthcare providers and surgeons for them to improve both performance and throughput in the O/R and also offers clinical data as a service (CDaaS) for medical devices companies, insurers and hospital administrators. As well, CareSyntax helps hospitals and ambulatory surgery centers optimize contracts with payers and large employers.
Caresyntax’s platform is currently used in over 3,000 operating rooms globally and supports surgical teams in more than 3 MM procedures annually. The value proposition in terms of improved O/R performance and throughput, lower LOS, superior patient experience and outcomes while improving surgeon performance in compelling and makes this a ’Smash Hit’ with enormous potential. For additional details, please check out https://caresyntax.com/platform/
A4.?? Patient Risk Stratification to proactively identify Care Gaps in High-Risk Patients [Easy Sell]
Healthcare Systems are challenged with segmenting their patient populations based on their risk profiles and co-morbidities. Tactically, this is further exacerbated when high risk patients are admitted for in-patient treatment in their facilities. Stratifying patients based on their 30-60-90 day re-admission risk in daunting for health systems and is mission critical for ensuring a high quality of care delivery, superior patient outcomes while minimizing risks of fines and penalties for the health system.
(A). Patient Risk Stratification (PRS) using a risk-based management approach:?is arguably, one of the most challenging aspects of PHM, demanding sophisticated machine learning, advanced predictive analytics software leveraging complex models to predict risk not only at an aggregate population level, but also at a discrete patient level. Leveraging AI and analytics solutions from vendors listed below, industry leaders are stratifying their patients based on 30 day re-admission rate risk, risk of overshooting their length of stay (LOS) and other key performance indicators (KPIs) into high risk patients (multi-morbid, catastrophic conditions like heart attacks, heart failure and cancer), medium risk (chronic conditions like diabetes, arthiritis, Alzheimers, Parkinsons et al) and low risk (preventable conditions).
(B). Risk based Treatment and Care/Case Management?involving care coordination, intervention by nurse case managers, and a care ecosystem comprising family, friends and social workers for the?highest risk patients.?For?medium risk patients,?this would involve enrolling them into a health plan funded wellness and disease management program for managing chronic conditions like diabetes to ensure that these can be managed well without exacerbation. Treatment of?low-risk patients?would be ‘business as usual’ with primary care and preventive services.
Solution providers providing AI+Analytics solution for value based care, population health management and care management include Lightbeam Health Solutions, Arcadia Health, Innovaceer, Ze Omega, Clarify Health Solutions, Aledade Analytics, Inovalon
A5.?? Automated Scanning of DICOM images to proactively identify patients at risk of an Acute Event like a heart attack, heart failure or stroke for Radiologists [Easy Sell]
Will AI replace Radiologists, or will it simply make them better than ever? The integration of AI in radiology is not about replacing human expertise but enhancing it, making diagnostic processes faster and more accurate. AI powered imaging analysis in Radiology offers a compelling use case for this truism.
?AI specifically, machine learning algorithms, have become invaluable in interpreting complex imaging data. These systems are trained on vast datasets of X-rays and other medical images, allowing them to recognize patterns and anomalies that might elude even the most trained eyes. Dr. Jane Smith, a leading radiologist, mentions,?"AI acts as a second set of eyes, catching details that can sometimes be missed due to human fatigue”. [Ref 3]
?One of the primary benefits of AI in radiology is its ability to enhance diagnostic accuracy. AI systems can analyze images with a level of detail and consistency that surpasses human capability, reducing the chances of misdiagnosis.?"The precision of AI tools ensures higher diagnostic accuracy, which is crucial for effective treatment planning,"?states Dr. Smith. [Ref 3]
Vendors offering AI/machine learning solutions for scanning and analyzing DICOM images in the cloud include Nuance, Intelerad, Viz.ai, Behold.ai as well as leaders like Philips, Siemens and GE Healthcare.
B. High Value Gen AI / LLMs Use Cases deployed in Healthcare
Figure 5. Portfolio of High Value Generative AI /? LLMs Use Cases in Healthcare. Copyright Andy De (2024). All rights reserved.
High-Value Use Cases Summary:
B1.?? Ambient Clinical Intelligence (ACI) for Automated Clinical Documentation, and uploading into EHRs, to reduce Physician and Nurse Fatigue with admin tasks and improve the Patient Experience [Smash Hit]
A recent study found that?primary care physicians are expected to work an impossible 27 hours per day, resulting in palpable physician fatigue, especially from administrative tasks like taking patient notes, editing them and then entering them in the EMR. [Ref 4]
?Ambient Clinical Intelligence (ACI) also referred to as ‘Ambient Listening” is arguably the Gen AI/LLMs enabled use case with the most traction in healthcare today. Ambient clinical intelligence (ACI) uses AI and voice recognition to automate clinical documentation reducing administrative burden and clinician burnout.
?Ambient Intelligence combines?sensors and generative AI to listen to the doctor-patient conversation (like a virtual assistant) and write SOAP notes (using smart phones or tablets) to automate administrative tasks like data collection, editing and clinical workflows integrated with the EMR.
?ACI also bakes context into the note structure. It automatically categorizes things like patient symptoms and treatment plans.?
?Companies offering Gen AI/LLM enabled ACI/Ambient Listening solutions include Nuance/Microsoft (DAX Copilot), Augmedix (a Commure company), Deep Scribe, Suki, Freed, Elion Health, Sunoh.ai from eClinical Works and Pieces Technologies.
B2.?? Conversational AI / Call Center chatbots for scheduling appointments and providing access to relevant resources with automated workflows (CALM) [Smash Hit]
Conversational AI with Language Models (CALM) is an LLM-native approach to building reliable conversational AI.?Conversational AI can?hold live conversations with users, meaning it can instantly answer phone calls and begin talking to a patient, understanding their needs and resolving their issues?– all with human-like responses and the autonomy to action things like appointments or document capture.
?Patients who access a Conversational AI will have their needs met faster and more effectively, as opposed to having to wait on hold to speak to a human agent.?
?Call centers within health systems can record a full transcript of each patient interaction and then create a summary to be filed against the patient’s records, using a combination of?Conversational AI+Gen AI. Sentiment analysis as part of this analysis and reporting can help providers track Patient Satisfaction over time and improve the Patient Experience.?
Vendors offering conversational AI platforms for Healthcare include Sales Force, Sensely, Your.MD, and others.
B3.?? Auto generate scripts for Physicians and Nurses bases on Clinical notes for superior bedside communication and bedside conversations to improve the Patient Experience [Easy Sell]
?Gen AI has been used to not only improve physician-nurse communication re: patients to improve responsiveness but also generate scripts for physicians and nurses to improve their bedside manner.
?Using an innovative staff-developed and driven acronym, IMOMW (I’m on my way), the study demonstrated significant positive outcomes such as a 10.5% rise in recommend facility net promoter score (NPS) patient experience survey scores, 13.4% increase in physician and nurse team communication, 5.4% increase in nursing communication, and a 5.3% increase in physician communication. [Ref 5]
C. High Value NLP / NLG Use Cases deployed in Healthcare
Figure 6. Portfolio of High Value NLP / NLG Use Cases in Healthcare. Copyright Andy De (2024). All rights reserved.
High-Value Use Cases Summary:
C1.?? Computer Assisted Coding (CAC) to read medical records and assign the correct codes for accurate diagnosis, treatment and claims management [Smash Hit]
Computer Assisted Coding (CAC) uses natural language processing (NLP) and machine learning algorithms to extract relevant information from medical records, such as patient symptoms, diagnoses, procedures, and medications, and then matches them to corresponding medical codes (like ICD-10, CPT) based on a comprehensive coding database.???????????????
This ultimately improves accuracy in claims management by minimizing manual coding errors and streamlining the billing process;?however, a trained medical coder still reviews and validates the suggested codes before finalizing them on the patient record.? Key benefits includes higher coding accuracy resulting in lower denials, improved coding efficiency and reduced labor costs associated with manual coding for healthcare organizations.
Vendors providing CAC solutions for claims management include AGS Health Solutions, Thoughtful.ai and others
C2.?? Automated Summary of Patient Chart / Physician’s notes at the Patient Bedside with actionable insights to improve Clinician and Nurse productivity [Smash Hit]
Natural Language Processing (NLP) systems can analyze clinical text to identify relevant clinical concepts, such as symptoms, diagnoses, medications, and procedures, and provide real-time decision support to healthcare providers.
NLP is also used to extract and analyze information from?EHRs, clinical trial?data, and biomedical literature for research purposes, enabling data-driven insights and?discoveries
?NLP algorithms can analyze large volumes of clinical text data to identify patterns, trends, and risk factors within patient populations, supporting population health management (PHM) and value based care (VBC)?initiatives and public health interventions.
?NLP for clinical notes is increasingly giving way to and are being supplanted by Gen AI/LLMs for Ambient Clinical Intelligence (ACI) or “Ambient Listening” applications described in the previous section.
C3.?? Automated Patient Sentiment Analysis with actionable insights and reporting from Press Ganey and EMR Patient Portal Data [Easy Sell]
Capture, consolidate, and organize verbatim patient comments from multiple sources into a centralized, intuitive, and easy-to-use platform.?NLP enabled analytics explains the “why” behind patient ratings so healthcare providers can accurately direct resources and guide improvement efforts.?
This enables providers to skip the manual steps of collecting, reviewing, and distributing patient comment data and pinpoint details that impact a single patient experience or identify common issues that affect patient cohorts. NLP enabled analytics automates this process, making it fast and easy.?Press Ganey has developed NLP/NLG powered solutions for Patient sentiment analysis.
D. High Value Medical Robotics and Robotic Process Automation (RPA) Use Cases deployed in Healthcare
Figure 7. Portfolio of High Value Medical Robotics and Robotics Process Automation (RPA) Use Cases in Healthcare. Copyright Andy De (2024). All rights reserved.
High-Value Use Cases Summary:
D1.??Robotic-Assisted Surgery / Minimally Invasive Surgery for faster Patient recuperation and lower LOS [Smash Hit]
While many interventions involving complex procedures or invasive surgeries will be done in person, there is significant innovation underway to enable AI enabled minimally invasive Robotic Surgeries such as those enabled by the?DaVinci Robots from Intuitive Surgical,?shown in the photo above, which offer the following measurable benefits:
We are witnessing significant innovations in Robotic Surgery like the?Renaissance Guidance System by Mazor Robotics?for spinal procedures, as well as robotics assisted surgery solutions from Stereoaxis, Globus Medical, Medtronic, Smith and Nephew, Stryker, and Zimmer Biomet.
D2.?? Exoskeletons and Robotics Assisted Physical Therapy to support and restore locomotion, movement and gait balance [Smash Hit]
Exoskeletons (as shown above) are robotic skeletal structures designed to help people who have been paralyzed or soldiers who have suffered serious injuries in the battlefield, and have lost most of their ability to walk or move. Strapping these robotic exoskeletons which can be controlled via a joy stick or voice commands enables these patients to move and become independent which significantly improves their quality of life.
D3.?? Automated Self-Service of Patient Administrative Tasks with Robotic Process Automation (RPA) [Smash Hit]
?"Automated Self-Service of Patient Administrative Tasks with Robotic Process Automation (RPA)" offered by companies like UI Path refers to?using RPA technology to enable patients to perform routine administrative tasks like appointment scheduling, updating personal information, or accessing medical records directly through a digital platform, without needing to contact a healthcare provider's staff, essentially creating a self-service patient portal powered by automated processes.?Examples of tasks automated include automated scheduling, medical/lab records access, billing inquiries, prescription refills etc.
Compelling benefits include reduced administrative burden and higher efficiency from healthcare staff (reduced costs), as well as enhanced patient engagement ensuring a superior patient experience and satisfaction.
D4.?? Robots capturing Vital Signs without direct caregiver contact [Easy Sell]
This is perhaps the single largest application which will see the adoption of robotics for non-contact based capture of vital signs like temperature, blood pressure, sugar levels and pulse oximetry (shown above), which is already happening in countries like China, Taiwan and Japan and will see significant adoption in the US, in the foreseeable future. While we have nurses or first responders scanning patients with touchless thermometers today, I can envision robots scanning not only patients for their temperature and oxygen saturation levels to diagnose if they have been infected at hospital receptions, but also at large retail stores, airline security checkpoints and even in company offices, to mitigate risks associated with Epidemics and Pandemics, going forward.
D5.?? Nurse Robots serving food and medicine to Patients and conversing in multiple languages [Easy Sell in APAC]
Nurse Robots (shown above) which are actually Automated Guided Vehicles (AGVs) have been deployed in Germany and other European countries, allows the automatic pick-up and delivery of:
?Robot Nurses?deployed at the Mongkutwattana General Hospital in?Bangkok, Thailand,?are used to dispense medication and make deliveries between the eight stations of the hospital. The robots?(which are actually AGVs) can even take the elevators by themselves and communicate in both Thai and Chinese. Technicians are able to adjust the robot’s settings via a touchscreen.
D6.?? Social Companion and Rehabilitation Robots for Senior Care [Easy Sell in Japan]
Social companion and Rehab Robots are becoming a reality in countries like Japan today. These are AI enabled human or animal resembling robots equipped with touch sensors, cameras and microphones enabling them to engage in some conversation with their owners/patients, who depend on them to remind themselves to check their vital signs and take their medications on time.?The?PARO Therapeutic robot?is an interactive device that looks like a baby harbor seal and is designed to provide the benefits of animal therapy without relying on live animals, to alleviate patient stress.?From stand-alone to Hybrid AI and its relevance for healthcare and life sciences
From stand-alone to Hybrid AI and its significance for Healthcare going forward
Given the rapid evolution of AI happening at an unprecedented pace, it is not sufficient to deploy these various modalities stand-alone but harness the potential of combining one or more modalities to drive value and ROI from a use case, which is referred to as Hybrid AI. This involves blending various AI techniques and models to achieve outcomes that surpass what any single AI approach could accomplish alone. [Ref 6]
LLMs, for example, are essentially probabilistic models that generate responses based on patterns in the data they’ve been trained on. This means they don’t “understand” the information in the way humans do – they just predict what’s likely to come next based on their training.
?This predictive ability is impressive when it comes to drafting emails, summarizing documents, or even brainstorming creative ideas. However, despite their remarkable capabilities, one of their most significant limitations is their propensity to generate false information with unwavering confidence, a phenomenon often referred to as "hallucination." In many applications, this isn't just a minor inconvenience—it can have serious implications.
?Consider, for instance, the use of a pure generative AI model in healthcare for diagnosing diseases. The potential for inaccuracies could lead to misdiagnoses, inappropriate treatments, or missed critical conditions, often with life and death implications. In such high-stakes scenarios, we need something more reliable, more precise, and more accountable. This is where Hybrid AI demonstrates its true value.
A Hybrid AI approach in healthcare might combine a traditional machine learning model trained on vast amounts of medical data with a generative AI component. The machine learning model could handle the intricate task of analyzing symptoms, test results, and patient history to generate a diagnosis with a high degree of accuracy. Meanwhile, the generative AI could step in to explain the diagnosis to patients in clear, understandable language, answering questions and providing additional information as needed. [Ref 6]
?This combination leverages the strengths of both AI types while mitigating their weaknesses. The result? A more accurate diagnosis coupled with better patient communication and understanding, which is win-win-win.
Another advantage of Hybrid AI is its potential to enhance explainability—a critical factor in building trust in AI systems. While some AI models, particularly deep learning neural networks, can be opaque in their decision-making processes, hybrid approaches often allow for more transparency. This is crucial in healthcare and life sciences and in applications where understanding the rationale behind AI decisions is as important as the decisions themselves. [Ref 6]
Bringing it all together – Andy De’s Reference Portfolio of 16 High Value AI Use Cases in Healthcare
Figure 8. Andy De’s Holistic Reference Portfolio of High Value AI Use Cases in Healthcare - to inform your Healthcare organization's AI Strategy and Blueprint. Copyright Andy De (2024). All rights reserved.
I have consolidated all of the 16 high value use cases across the multiple modalities (machine learning, Gen AI/LLMs, NLP/NLG, Medical Robotics and Robotic Process Automation (RPA)) into this comprehensive and holistic reference portfolio leveraging the very same strategic segmentation framework. This clearly demonstrates the value of deploying the appropriate AI (or hybrid AI) modality for specific use cases to assure success, mitigate risk and deliver value and ROI to your healthcare organization vs. deploying Gen AI/LLMs alone given that this is the least mature of all the AI modalities
Here is another representation mapping the AI modality to the use case for intuitive correlation and reference that will likely inform your AI Strategy and Blueprint below:
Figure 9. Andy De's Reference Portfolio of 16 High-Value AI Use Cases deployed in Healthcare mapping the enabling AI modality to each Use Case - to inform your Healthcare organization's AI Strategy and Blueprint. Copyright Andy De (2024). All rights reserved.
These are real-world high value use cases enabled by the portfolio of AI modalities delivering value to provider and payer organizations today and not “solutions looking for problems to solve and models to train”!
Does my ‘AI Innovation Portfolio of high value use cases' (a "living library") for healthcare providers and payers resonate with you?
?Are there critical AI modalities or high-value use cases relevant for healthcare that I have not included or addressed with this portfolio that should be included here?
Please feel free to share this detailed blogpost with your colleagues via Linked-In, and connect with me if you want to discuss your AI Strategy, Blueprint or Portfolio for your healthcare organization or function. You can email me at [email protected].
If interested in consuming this POV as a vodcast, please check out this YouTube video below:
As always, I welcome your comments and feedback here on?this blogpost,?and via email at [email protected]
Disclaimer: The perspective and views expressed in this Blog post are my own and do not represent those of my current or previous employers.
#AI #AIportfolio4healthcare #MachineLearning #NLP #NLG #GenAI #LLMs #HybridAI #MultimodalAI #MedicalRobotics #RPA
REFERENCES:
1.?? ’Eager Sellers and Stony Buyers – Understanding the Psychology of New Product Adoption’ by John T. Gourville, Harvard Business Review (HBR), June 2006
2.?? ‘Sepsis Detection has the potential to prevent thousands of deaths’, by Will Kirk, Johns Hopkins University, published in the Hub.
3.?? ‘AI in Radiology: A Replacement for Doctors or a Tool for Enhancement?’ in the Open Privilege Newsletter, May 15th, 2024.
4.?? ‘How Ambient Clinical Intelligence (ACI); can change healthcare for good’ by Dr. Gabriela Meckler, Freed, October 22nd, 2024.
5.?? ‘Improving Nurse-Physician Bedside Communication using a Patient Experience Quality Improvement Pilot Project at an Academic Medical Center’, Justin Wang et al. published in Cureus, National Library of Medicine, March 2024.
6.?? ‘Why Hybrid AI is the next big thing in Tech’ by Bernard Marr in Forbes Innovation, October 2, 2024
??
Senior Technology Executive | Board Member | Health - We have the best clients and we are moving the needle in Healthcare. Everyday.
1 个月Andy Dé absolutely love this. Especially the quote, "There's a significant disconnect between the current technological state of hospitals, where outdated tools like fax machines are still in use, and the expectations of AI vendors." This is completely accurate. Start with the basics.
Very comprehensive and insightful article around Agentic AI. the 2x2 matrix views are quite effective as simplifying complex perspectives. Well done!
Transformational 3X Chief Marketing Officer (CMO) I Healthcare & Life Sciences AI+Analytics Innovation Visionary, Evangelist, Thought+Change Leader, Board & Company GTM Advisor - Impacting $ Bn in Enterprise SaaS Revenue
1 个月To consume these insights on video, please check out my recent podcast / vodcast with Matthew Switzer, co-founder and principal of #HealthcareSalesPerformance on #YouTube and let me know your perspectives and insights on this thread below...Happy New Year! https://www.youtube.com/watch?v=YBxOOGXfaTM
Medical Device, Healthcare and Technology Sales Performance Training & Consulting | healthcaresalesperformance.com
3 个月Andy Dé this is outstanding, thank you for putting this together. As we consider modes of supporting commercial organizations seeking to help healthcare orgs gain ROI through implementation, I'm ever more convinced of the requirement to find creative and productive ways to unify the clinical and technological conversation earlier, particularly in some environments, as you say, that still use fax machines!
Co-founder SeedtoB Capital | Co-founder Jvion (exit 2018)
3 个月Great post Andy Dé! Must read for those getting into AI in Healthcare!