In the wake of the coronavirus pandemic, healthcare organizations had to double down on their digital transformation projects.
From massively adopting telehealth systems, which in usage in the USA alone, to populating electronic health records (EHR) and practice management solutions with robotic process automation (RPA) bots to compensate for the worsening workforce shortages, hospitals, labs, and medical R&D units are becoming increasingly digitalized.
But does this digitalization always translate into increased efficiency, cost reductions, and, ultimately, better patient care?
Recent studies suggest otherwise. The above-mentioned telehealth products, for instance, may require nurses to perform twice as much work as they’d do tending to patients in person. New technology could be responsible for up to 50% of the annual healthcare cost increases. And the amount of data generated by the amounts to 30% of all information in the world (spoiler alert: by 2025, it could be 36%).
Could machine learning (ML), a subset of artificial intelligence (AI) that involves training algorithms to process large volumes of structured and unstructured data with little to no assistance from humans, be the ultimate answer to the numerous healthcare software development problems?
Let’s find that out!
3 Ways Machine Learning Redefines Healthcare Software Development
ML Helps Break Silos in Healthcare Systems
Despite all the innovations (or, rather, because of them!), healthcare data interoperability remains a pressing issue for most medical organizations.
The information stored in various healthcare systems, from EHRs to laboratory and pharmacy software, may exist in different types and formats. Duplicate and incomplete medical records are not infrequent. And far too many healthcare organizations continue to use highly customized medical systems that lack APIs for seamless and secure data sharing.
Machine learning has tremendous potential to enhance traditional extract, transform, and load (ETL) operations, breaking silos in healthcare systems and improving overall efficiency, collaboration, and patient outcomes.
One example of an ML-driven healthcare software integration project comes from Majo Clinic, whose Mayo Clinic Platform helps aggregate miscellaneous data from across their network and partner organizations. Another example is Truveta, a deep EHR platform that uses machine learning to scavenge medical data from 28 healthcare systems and structure it into the unified Fast Healthcare Interoperability Resources (FHIR) format.
ML Streamlines Healthcare Data Processing
Machine learning models play a crucial role in speeding up medical information analysis and extracting insights from large and complex datasets.
Some of the most potent ML applications in this field of healthcare software development include:
- Medical diagnosis. Machine learning models can be trained to detect various medical conditions and diseases from different types of data, such as medical images (e.g., X-rays, MRI scans), patient records, and genetic information. This can lead to earlier and more accurate diagnoses as algorithms need less time to absorb and interpret large amounts of medical data than human doctors.
- Novel treatment strategies. Using a variety of machine learning techniques, including natural language processing (NLP), neural networks, and clustering, ML-based healthcare solutions traverse large databases in search of recurring patterns associated with particular diseases. Sophisticated deep learning algorithms may dig as deep as protein structure, modeling a patient’s reaction to different therapies and recommending the best approach. A recent study from the Vienna University of Technology suggests ML can come up with more effective treatment strategies for patients with blood poisoning than its human counterparts.
- Drug discovery and development. ML-driven drug discovery is gaining momentum in the post-pandemic world. The leading pharmaceutical companies, such as AstraZeneca, Bayer, and Pfizer, infuse their IT systems with intelligent algorithms to accelerate chemical compound screening and identify potent drug candidates faster, discover new medications, evaluate drug efficacy, and optimize dosages. Machine learning can also assist healthcare organizations in recruiting patients for clinical trials by conducting rigorous analyses of patient records.
However, when embarking on a machine learning project, healthcare organizations should consider the challenges imposed by using incomplete, non-representative, and erroneous data in ML model training. Besides delivering inaccurate predictions, improperly trained models may display a bias toward particular patient groups, downplaying or exaggerating health risks.
ML Increases Workflow Automation
Building on advanced data sourcing and analytics capabilities, machine learning-based healthcare software solutions unlock new opportunities for process and workflow automation.
Key applications of ML in healthcare software systems include:
- Intelligent chatbots and virtual health assistants are taking over post-discharge consultations, appointment scheduling, and patient engagement tasks
- Intelligent process automation (IPA) tools source data across integrated healthcare systems and enter it into patient records
- Medical inventory management systems provide real-time data on equipment usage and supply levels and automatically restock items a hospital is running low on.
- Remote patient monitoring (RPM) solutions combining connected devices, ML models, and user-facing apps to track patients’ vitals, physical activity, and medication intake
- Self-service business intelligence (BI) solutions empower healthcare workers to generate complex reports without involving IT specialists and make better-informed decisions.
To implement such ML systems in a risk-free way, healthcare organizations may turn to a like Symfa to build a solution or procure specialized software as a service (SaaS) products, such as Health Catalyst and Automation Anywhere.
On a Final Note
With healthcare costs rising across the globe (an 8.8% increase in 2022) and medical workers quitting at an alarming rate, healthcare organizations must approach their digital transformation initiatives with caution.
This, above all, means investing in technology solutions that alleviate the burden of administrative work off your employees and allow them to devote more time to meaningful tasks — i.e., patient care.
Undoubtedly, machine learning is one such game-changing technology, and given recent advances in artificial intelligence, more healthcare companies can experiment with ML on a lower budget. And yours should too.