A new practical way to implement predictive analytics in healthcare

Most healthcare professionals in Asia-Pacific now recognise the necessity of adopting AI technologies to complement care delivery, foster clinical and operational efficiency, and improve equitable access and health outcomes amid rising demand and workforce shortage.

For instance, a majority of the region’s healthcare professionals surveyed for the latest Philips’ 2025 Future Health Index report believe digital technologies, including AI and predictive analytics, could help reduce hospital admissions and facilitate earlier interventions to save lives. They were also found to be actively involved in developing these technological solutions at their organisations. 

Yet, there remain persistent concerns over trust and implementation. The Philips survey, for one, found that these professionals worry their technologies are not catered to their needs. They also noted potential data biases in AI applications that could widen disparities in health outcomes. 

A recent study in the United States further delved into major challenges in the implementation of predictive analytics in healthcare. Rohan Desai, a business intelligence analyst, noted that these are data integration, quality, model interpretability, and clinical relevance. 

In a follow-up article, published in Scientific Research Publishing’s Journal of Intelligent Learning Systems and Applications, he reviewed these challenges and proposed a roadmap for future research and practical implementation of healthcare predictive analytics.

The roadmap highlights the implementation of hybrid machine learning models, including stacking, boosting techniques, and hybrid neural network-random forest models. This hybrid approach leverages the unique strength of each technique; for example, stacking various models can reduce bias and variance, boosting can enhance model performance iteratively, and hybrid networks can capture complex nonlinear patterns while maintaining interpretability. 

His proposed framework emphasises the right combination of standardised data integration, advanced preprocessing, hybrid modelling, and ethical safeguards to move healthcare predictive analytics beyond theory to a trusted, practical tool for clinical decision-making. 

As a data modeller at R1 RCM, a US-based revenue cycle management solution provider, Desai focuses on transforming data into actionable insights, with relevant skills in data modelling, standardisation, and visualisation, as well as machine learning. On the side, he volunteers with Red Cross as a data analyst, judges science and technology competitions, and mentors students in innovation challenges. 

Desai discussed with Healthcare IT News more about his proposed predictive analytics implementation framework in healthcare and how it can be practically applied in the APAC context. 

Q. Can you expound on the practical application of your proposed framework? What is its value proposition in terms of costs and ease of implementation, adoption, and use among clinicians?

A. Sure! The core idea behind my framework is to make predictive analytics more usable for everyday decision-making in healthcare, especially when it comes to revenue cycle operations like claim denials and patient payment behaviours. What makes it practical is that it builds on open-source tools and existing data flows, so hospitals or clinics don’t need to overhaul their systems to get started. It’s designed to plug into standard data formats (like HL7 and CSV), and it can run on lightweight cloud environments or even local servers.

In terms of cost, the approach is relatively lean; it uses Python libraries like Scikit-learn and XGBoost, and the infrastructure demands are modest. From the clinician’s perspective, the goal isn’t to throw another dashboard at them, but to quietly support operational teams behind the scenes. For instance, it can help predict accounts likely to go unpaid or identify coding errors before they become rejections. So, adoption doesn’t mean more screen time for doctors – it’s more about streamlining administrative workflows that support care delivery.

Q. What can you say about the healthcare analytics deployment and use landscape in Asia-Pacific? What do you see are major challenges in the uptake and use of the technology?

A. From what I’ve seen and read, healthcare analytics in the Asia-Pacific region is growing fast, but it’s not without challenges. On one hand, there’s a strong interest in digital transformation, especially in places like Singapore, India, and parts of Southeast Asia. On the other hand, there are big differences in digital maturity between public and private institutions, and even between urban and rural facilities.

One major hurdle is data quality and access. A lot of systems still rely on paper records or fragmented digital tools, which makes analytics tough to implement. There’s also a gap in trained personnel, both technical folks who can build the models and clinicians who can interpret the results meaningfully. Lastly, change management plays a big role. If hospital leadership isn’t fully on board, even the best tools struggle to gain traction.

Q. Does your proposed framework have potential application in low-resource settings? How will it encourage uptake among clinical users there?

A. Yes, I definitely think so. One of the things I was mindful of while designing this was to avoid dependency on expensive software or proprietary platforms. The whole thing runs on open-source tools and is modular, so teams can start small – maybe just using the denial prediction module and add on over time.

For low-resource settings, the framework can be adapted to work with whatever data is available, even if it’s incomplete or messy. It’s more about identifying trends than getting perfect precision. Clinician uptake could be encouraged by keeping the interface simple (or even avoiding interfaces altogether if insights can be delivered through existing reports). Also, building trust is key, so early pilots would ideally involve close feedback loops with the users.

Q. Does your proposed framework also address data integration and interoperability challenges, and how?

A. To some extent, yes. The framework is designed with flexibility in mind. It supports common healthcare data formats like HL7, FHIR, and standard CSV exports from EHRs and billing systems. I’ve tried to make the ETL layer adaptable, so data from different sources can be cleaned and aligned before analysis.

That said, full interoperability still depends a lot on how standardised the upstream systems are. My focus was on making the data layer “forgiving,” so even if the source systems aren’t perfect, the model can still work. It’s not a silver bullet, but it helps bridge some of the gaps.

Q. Can you share plans and future collaborations with healthcare providers/clinics in operationalising your proposed framework?

A. At this point, the framework is still in an early stage. I’ve tested it using open and anonymised datasets from platforms like Kaggle, and while the initial results are promising, it needs to be trained and refined on more complex, real-world data.

I haven’t partnered with any hospitals yet, but I’m very interested in collaborating with research-focused institutions. If I had to pick an ideal environment, it would be places like Johns Hopkins, Mayo Clinic, or Cleveland Clinic in the US, or top-tier centres in India like AIIMS, Narayana Health, or Tata Memorial. These organisations have the infrastructure and multidisciplinary teams that can really help put the framework through its paces.

Long term, my goal is to develop a toolkit-style version that can be piloted in a mid-sized hospital or teaching facility. Once it’s been tested and fine-tuned, I’d love to make it available to a broader audience – ideally in a format that’s accessible for both large systems and smaller clinics.