Media Article

Engineering breakthroughs defining the future of healthcare

Vaishali Nambiar
EVP, Medical Technology Market,
CitiusTech

Mar - 11

Maya has been diagnosed with rare stage 2 lung cancer. A routine lab test revealed the condition. Oncologists who examined Maya are huddled in an adjoining room for a discussion to decide the course of action. As the team deliberates, something remarkable pops up on the dashboard of the clinical decision-support system. Powered by real-time AI, this software has been parallelly analyzing the patient’s medical history, current vitals, and symptoms. It recommends a rare but life-saving treatment that was not previously considered by the team.

This innovation was not born in a lab. It was the result of several software engineers relentlessly working to push healthcare systems toward patient-centricity.

Such moments in the not-so-distant future highlight the potential of engineering in healthcare. Today, engineers don’t just write code; their ability to create pathways to save lives by harnessing software engineering is redefining patient care. At the crossroads of AI, cybersecurity, analytics, and GenAI, engineering productivity has emerged as a potent force to elevate healthcare to the next level.

Democratizing innovation

Innovation in engineering happens at the overlap of reality and science fiction. Engineers must be able to predict and identify emerging technologies that can solve problems in the healthcare domain. They must be able to get unconnected technologies to work together to deliver value. All this calls for empowering every engineer to become an agent of innovation.

Democratized innovation cuts across teams and a cycle of value that cascades from internal teams to clients and ultimately to patients.

Democratized innovation is driving advancements in AI-assisted diagnostics. AI engineers have trained algorithms on vast datasets to interpret images, detect anomalies in medical images and prioritize cases. AI is fast-tracking the diagnosis and prognosis of conditions like cancer and fractures Research studies in the diagnosis of skin diseases using AI algorithms have reported accuracies of over 97%.^[1]
Advanced computing also plays a critical role here. By leveraging in silico research models^[2] and processing vast amounts of data, engineers enable personalized care pathways.
Multi-modal data processing^[3] that allows a wide variety of data to be collected and analyzed is yet another engineering innovation that is poised to revolutionize clinical practice by integrating artificial intelligence with traditional diagnostic modalities.

The above instances highlight how innovation happens due to the cross-pollination of ideas across fields of healthcare and engineering.

Breaking down silos for smooth Interoperability

Software engineering in healthcare gets truly unleashed when interoperability barriers between systems cease to exist. Engineers are tasked with designing systems that facilitate a common exchange of data across platforms, enabling real-time, boundaryless access.

Providers and payers are shifting healthcare data sharing from HL7 (Health Level 7) standards to the newer FHIR (Fast Healthcare Interoperability Resources) web-based standards. FHIR is being embraced as the de facto data exchange standard across devices.
Even EMRs and EHRs are being integrated with robust data standards to facilitate improved clinical decision-making and promote operational efficiencies. The Healthcare Interop solutions market is predicted to grow more than three times the current size^[4] to touch $10.9 billion in just a decade!

Engineers are trying to codify proprietary knowledge using NLP and AI and turn it into repositories that provide actionable tools to assist other teams and healthcare providers. This approach ensures innovation is not siloed—it is accessible by all.

Aligning innovation with patient outcomes

One of the most inspiring aspects of software engineering’s impact on healthcare is its alignment with patient-centric goals.

Clinical decision support systems (CDSS) especially in radiology, nephrology and oncology have taken off. AI applications assist in radiology image analysis^[5], enhancing the detection and diagnosis of conditions such as tumors. They help in oncology^[6] via tumor profiling and treatment planning.
Large-scale patient-centric testing is now possible (as opposed to traditional systems testing), thanks to remote monitoring devices that can hook up to AI applications. Personalized medicine will integrate personal health devices and support for BYOD.
Predictive analytics: AI systems in ICUs^[7] will predict sepsis risk, cardiac deterioration, hemorrhage risk, and respiratory failure events in advance, to jumpstart preemptive care. This will directly improve critical care outcomes. AI algorithms are being built and tested to forecast disease progression from Chronic Kidney Disease (CKD) to End Stage Renal Disease (ESRD) in case of dialysis patients and identify hospitalization risk of patients with comorbid conditions.
Scheduling, billing, and beyond: Transforming clinic management with AI – There is a huge focus on automating administrative tasks such as benefits verification, scheduling patient appointments, managing staff schedules, automated claims generation, etc. This will not only reduce human efforts and errors but also reduce burn out of care providers and bring down the overall cost of care.

Such innovations, while improving health outcomes, also increase the utilization of assets. This double effect can be traced back to the efforts of engineering teams.

Security and trust - two non-negotiables of engineering

Consider these two statistics

Ransomware attacks reduce in-patient volume by 14% to 17%^[8] during the first week of the attack.
80% of organizations require over a week to recover^[9] from ransomware attacks with 37% taking more than a month.

Healthcare innovation must be safeguarded by detailing the security aspects and addressing privacy concerns. Engineers handle these dual imperatives by employing some novel methods. By building secure API ecosystems and adhering to stringent data protection protocols, they ensure that innovation does not come at the cost of trust.

The integration of AI in diagnostics is gaining traction around us. Data poisoning and adversarial attacks significantly threaten healthcare systems. Think of the mayhem an attacker can cause by injecting poisoned data into a model trained to identify malignant tumors - it could misclassify a malignant tumor as benign!^[10]

Applying automation in quality assurance tasks, and embedding security right from the point of model training are innovative ideas being implemented by engineering teams as they work on mitigating such risks. They are even incorporating continuous anomaly detection in AI models along with validation features to ensure these models work within the guardrails of fairness, accuracy, and safety. These are essential for continued patient trust and regulatory compliance.

As entire systems get offloaded to the cloud, secured access to stored data too becomes imperative.

Secured data exchange without affecting interoperability becomes key to the smooth functioning of healthcare systems.

API gateways like the Google Cloud Healthcare API^[11] implement the core principles of interoperability frameworks like FHIR.
Managed detection and response (MDR) services are being employed to address EHR-specific threats that can be a cause of concern when it comes to safeguarding patient-sensitive data.

All the above instances showcase how integral software engineering has become in designing trustworthy healthcare systems – right from the simplest software on wearables to gargantuan systems that span the whole ecosystem.

Engineering the future of healthcare

New technologies keep emerging. Some thrive. Some do not make it past the hype cycle. Software engineering teams are best positioned to examine their viability in addressing healthcare challenges. The benefits of an integrated approach far outweigh the limitations of cherry-picking just a handful of technologies. Each technology enhances and complements the others, creating a ripple effect.

Engineering teams in the healthcare space are working at the intersection of several of these technologies. This makes them the harbingers of the next set of innovations to help realize a resilient, digital, patient-centric healthcare ecosystem. One that operates with built-in trust and agility, and provides the optimal outcome in each scenario.

For engineers, the journey ahead is both challenging and one that will test your mettle. Remember, you are not just contributing to healthcare’s digital transformation—you are defining it!