In this strategic exploration into the forefront of HealthTech innovation, the convergence of personalized healthcare and cutting-edge technologies is strategically reshaping the patient care landscape. In this dynamic sphere, leaders and founders aren’t passive spectators; but visionaries seeking to harness the full potential of personalized medicine.
As we delve into the significance of personalized healthcare within the HealthTech industry, it becomes evident that the era of one-size-fits-all approaches is making way for a more tailored and patient-centric paradigm. At the heart of this transformation lie two key players: Artificial Intelligence (AI) and Machine Learning (ML). These transformative technologies are not merely buzzwords but powerful tools that have matured rapidly, exemplified by groundbreaking innovations like GenAI.
In particular, our exploration zooms in on Clinical Decision Support with Case-based Reasoning (CBR)—a beacon of innovation that tailors treatments to individual patient needs. This blog unveils the workings of Clinical Decision Support, demystifying the intricate processes that underpin its effectiveness.
Join us as we navigate the intricate tapestry of personalized healthcare, where AI and ML take center stage in predicting treatment effectiveness and offering Case-based Reasoning for diagnoses. The recent maturation of AI, coupled with its newfound accessibility and affordability, opens doors for even small and mid-sized businesses to partake in the transformative journey of HealthTech. This blog will explore how these advancements contribute to a revolutionary shift, empowering practitioners and benefiting patients alike.
Current Landscape of Personalized Healthcare
Fueled by a range of groundbreaking genomic advancements, personalized medicine (PM) takes center stage in healthcare systems, particularly in the United Kingdom (UK), the United States (US), and the European Union (EU). Positioned as an emerging field, PM utilizes specific biological markers, often genetic, for more precise diagnosis and individualized disease management.
The Human Genome Project, completed in 2003, marked a pivotal shift toward personalized medicine by enabling the identification of genetic variations associated with thousands of diseases. This breakthrough paved the way for genomics, facilitating fast, affordable, and accurate DNA and RNA sequencing. In the context of diseases like cancer, genomics plays a crucial role in tailoring treatments to individual patients based on their unique genetic information. By deciphering the complex interplay between genes and the environment, DNA sequencing identifies inherited genes that elevate susceptibility to specific cancers.
In recent times, a novel genomics strategy known as somatic mosaicism is pushing the boundaries of personalized medicine by customizing therapies to the unique cells of individual patients.
Challenges in Predicting Treatment Effectiveness
As the global adoption of Personalized Medicine (PM) technology, including in Southeast Asia (SEA), gains momentum, the implementation encounters a formidable challenge—high costs. Concurrently, the pursuit of Universal Health Coverage (UHC) by healthcare systems, aiming for accessible health services without imposing financial strain, faces a critical hurdle in overcoming financial constraints for PM.
Informed by the experiences of “early adopters,” a series of pressing issues surfaces: a dearth of evidence for clinical utility, inadequate awareness among providers, patients, and families, limited access to genetic testing, the absence of reimbursement structures for genetic testing, the essential need for real-time integration of test results with Electronic Health Records (EHR) and Clinical Decision Support (CDS) tools, and ethical, legal, and social concerns. Essentially, economic challenges and operational obstacles emerge as primary impediments to the progress and effective implementation of personalized medicine.
The Role of AI and ML, and CDSS in Healthcare
The healthcare landscape is witnessing a transformative wave driven by AI and ML particularly in the realm of Clinical Decision Support Systems (CDSS). As these technologies revolutionize medical practices, it becomes crucial to address key considerations for enterprises looking to harness their potential.
AI and ML in Healthcare: Navigating Data Challenges
The promise of AI and ML in healthcare is immense, with advancements in automated diagnostics, drug design, and treatment optimization. However, the efficacy of these technologies hinges on the quality of training data. Collating, cleansing, and annotating vast datasets are essential tasks, demanding a meticulous approach to ensure accuracy and relevance. Simultaneously, stringent measures to uphold patient privacy must be a top priority. Enterprises and HealthTech companies operating in this space need to navigate the delicate balance between data accessibility and safeguarding sensitive patient information.
The Collaborative Synergy: Healthcare and Data Specialists
Creating robust AI algorithms and establishing ML systems for continuous learning necessitate a collaborative effort between healthcare professionals and data specialists. The marriage of medical expertise with data science is essential to refine algorithms that not only diagnose accurately but also evolve with the dynamic nature of healthcare. Organizations must assemble interdisciplinary teams, fostering a synergy between healthcare practitioners who understand the nuances of patient care and data specialists equipped to harness the power of algorithms for improved outcomes.
Interoperability in CDSS: Crafting Secure Data Exchange
CDSS play a pivotal role in enhancing healthcare quality, safety, and efficiency. However, their effectiveness is contingent on seamless data interoperability. Enterprises venturing into this domain must grapple with the challenge of integrating CDSS with the myriad systems used in healthcare facilities. The expertise required extends beyond the development of CDSS itself, encompassing the setup of secure data exchange protocols within the facility and with external entities. Crafting a robust and secure infrastructure for data sharing is imperative to maximize the impact of CDSS on patient care.
CDSS Customization Based on Specialities
Operational in low-resource settings, CDSS proves advantageous where medical resources and qualified clinicians are limited. It is crafted to replicate the reasoning of medical professionals but in a faster, less error-prone, and cost-effective manner. It is categorized into knowledge-based and non-knowledge-based systems. Knowledge-based CDSS relies on medical guidelines, while non-knowledge-based systems use Machine Learning (ML), analyze historical clinical data and develop predictive models for clinical outcomes based on new inputs. ML-based CDSS holds significant promise in clinical practice by enhancing decision accuracy and minimizing medical errors through its objective, data-dependent decision-making logic. However, their effectiveness hinges on the quality and quantity of the data provided and is typically limited to one speciality. Multi speciality facilities may require multiple CDSS which introduces yet another layer of data integration
Another category within CDSS is the Case-Based Reasoning (CBR) system. The CBR model is an impactful paradigm that enables the application of knowledge derived from specific cases, previously encountered situations, or distinct patient scenarios to solve new cases. In radiotherapy for brain cancer patients, a CBR system can draw on past case data to help determine the optimal number of beams and ideal beam angles. Subsequently, medical physicists and oncologists would assess the suggested treatment plan for its practicality.
Benefits of CDSS
Implementing CDSS in your organization can revolutionize workflow and yield significant benefits.
Improved Diagnostic Process
- CDSS provides 24/7 availability for diagnosing patients, efficiently translating symptoms into diagnoses.
- Integrating CDSS with a robust EHR/EMR system enables instant access to patient data, allowing for quick and accurate suggestions for possible diagnoses.
- Leveraging advanced technologies like big data analysis and cloud computing enhances the system’s capabilities, identifying undiagnosed conditions and predicting treatment outcomes.
Enhanced Quality of Care
- Modern CDS software analyzes data and generates automatic alerts for routine visits, planned medication orders, or potential health risks, particularly beneficial for chronic disease management.
- Alerts for critical conditions, such as low-density lipoprotein (LDL) levels in cardiovascular disease patients, ensure timely intervention.
- The system can schedule follow-up visits and home care, enhancing overall patient care and reducing hospital readmissions.
Medication Safety Ensured
- CDSS generates event-driven alerts, preventing medication errors by recommending changes in medication type or dosage based on new patient data.
- Automation in the order process reduces errors caused by distractions, as identified in a recent NCBI study.
- DDI alerts mitigate the risk of drug-to-drug interactions, enhancing patient safety during medication prescriptions.
Streamlined Administrative Functions
- CDSS reduces administrative burdens by ensuring compliance with the latest code standards and providing document templates.
- It aids physicians in clinical documentation, ensuring accuracy and consistency while flagging data inconsistencies that require attention.
- Automation in admission, transfer, and discharge processes and standardized order sets simplifies paperwork and improves efficiency.
Lower Readmission Rates and Cost Savings
- Improved diagnostic practices and effective treatment, coupled with scheduled home visits, contribute to better patient outcomes and reduced readmission rates.
- CDSS minimizes human errors, automates critical decision processes, and optimizes medical imaging orders, resulting in significant cost savings for medical businesses.
Impact on Patient Care
Researchers have employed diverse AI-based methodologies, including machine and deep learning models, to identify diseases such as skin, liver, heart, and Alzheimer’s that require early diagnosis. For instance, in a study conducted by Dabowsa et al. (2017), a backpropagation neural network was utilized for diagnosing skin diseases, achieving exceptional accuracy through real-world data gathered from the dermatology department. Ansari et al. (2011) employed a recurrent neural network (RNN) for diagnosing liver disease hepatitis virus, achieving a 97.59% accuracy, while a feed-forward neural network achieved 100%. In the work of Owasis et al. (2019), a 97.057 area under the curve was obtained using a residual neural network and long short-term memory for diagnosing gastrointestinal disease.
In Closing: Navigating the Future of Healthcare Innovation
The fusion of personalized healthcare and cutting-edge technologies emerges as a potent force in shaping the future of patient care. The transformative potential of AI, ML, and CDSS is not just theoretical but a tangible reality that holds promise for a healthcare revolution. Drawing upon our in-depth domain knowledge and technological proficiency, we collaborate with key stakeholders throughout the healthcare ecosystem, including patients, healthcare service providers, payors, tertiary care organizations, and HealthTech companies to develop a comprehensive healthcare solution that is more personalized, efficient, and patient-centric than ever before.
- Dabowsa N, Amaitik N, Maatuk A, Shadi A (2017) A hybrid intelligent system for skin disease diagnosis. In: Conference on engineering and technology, pp 1–6. 10.1109/ICEngTechnol.2017.8308157
- Ansari S, Shafi I, Ansari A, Ahmad J, Shah S. Diagnosis of liver disease induced by hepatitis virus using artificial neural network. IEEE Int Multitopic. 2011 doi: 10.1109/INMIC.2011.6151515.
- Owasis M, Arsalan M, Choi J, Mahmood T, Park K. Artificial intelligence based classification of multiple gastrointestinal diseases using endoscopy videos for clinical diagnosis. J Clin Med. 2019;8:786. doi: 10.3390/jcm8070986.