Metropolis Improves Lab Efficiency Using Middleware, Reduces Manual Work by 75%

In the last few quarters Metropolis Healthcare has undertaken a major digital transformation exercise aimed at  both the internal and external users. The diagnostics chain has launched a consumer app, a partner app for the partners including a lead automation mechanism. 

A middleware has been rolled out for the Pathologists. It sits between the laboratory machines and the front-end reporting dashboards. An auto authorisation feature has been added in the middleware, which separates those reports that are normal and do not require any expert analysis. It has helped improve the turnaround time for selected test panels.

Together, these initiatives have not just strengthened operational backbone, but also elevated customer experience, quality, consistency and scalability, claims the company. FE CIO speaks with Pinakin Shah, Chief Information Officer, Metropolis Healthcare, to know more about the digital initiatives

What were the key digital initiatives implemented in H1 FY26, and what RoI or operational gains have been realised ?

In H1 FY26, we focused on strengthening our digital ecosystem with a sharper emphasis on customer experience, clinical consistency and operational efficiency. 

A significant part of this effort was the enhancement of the Metropolis mobile app, which now offers a more seamless end-to-end patient journey. Key improvements include simplified test booking for individuals and families, intelligent test and package recommendations through a scientifically curated algorithm, real-time sample tracking and enriched report insights that support better health decisions. Features such as loyalty rewards, personalised health tools and an integrated lab locator have further improved engagement and convenience for users.

We also standardised our reporting module across laboratories, ensuring uniform interpretation for clinicians regardless of where the test is processed. 

Additionally, the newly launched Partner App for collection centres and B2B partners has streamlined order management, billing and report tracking, reducing dependency on the sales support team and improving turnaround times.

On the operations front, AI-driven call quality monitoring was expanded to cover 100% of voice interactions, significantly improving compliance visibility and enabling more focused training interventions. Enhancements to the call centre ecosystem—including IVR-based routing and digital agent interfaces such as WhatsApp integration—have helped reduce monthly call volumes and lowered Average Handle Time (AHT) by 15–20%, contributing to better service efficiency.

What has been the impact of middleware auto-authorisation on TAT and efficiency?

Middleware auto-authorisation has had a notable impact on laboratory efficiency. By enabling rule-based auto-verification for stable parameters, manual review workloads have reduced by nearly 75%. Turnaround times for selected test panels have improved by about 5%, and result release has become far more consistent, particularly during peak hours. 

Importantly, this has also allowed pathologists to channel their attention towards complex interpretations and exception cases, strengthening our ability to meet TAT commitments across high-volume centres.

What were the biggest challenges—technical, cultural, operational?

We encountered several challenges across the technical, cultural and operational dimensions. 

Technically, integrating new digital layers with existing legacy systems—especially across diverse Laboratory Information Management System (LIMS) platforms—proved demanding. 

Culturally, shifting teams from established manual processes to automated, rule-driven workflows required a significant mindset change, and there was initial hesitation in embracing AI-driven decision support tools. 

Operationally, coordinating digital workflows with real-time inputs from laboratory operations, logistics and service call centres required close alignment. 

A further foundational challenge was maintaining data quality and standardisation across more than 750 towns.

How was change management handled across India?

Change management was executed through a structured, phased programme. 

Teams across phlebotomy, laboratory operations, logistics and customer care received role-based training to prepare them for the transition. 

Rollouts were introduced in phases to ensure operational comfort before nationwide expansion. We also established continuous feedback mechanisms, allowing issues to be addressed promptly, and shared adoption metrics with stakeholders to maintain engagement. 

This approach ensured strong adoption within the first quarter of rollout.

What did the transformation team look like?

The transformation was led by a hybrid, cross-functional structure comprising digital product and engineering teams, data science and AI/ML specialists and cybersecurity and infrastructure teams. 

We also included departmental business stakeholders and SMEs from sales, operations, call centres and laboratories who provided essential domain insights. 

A centralised PMO oversaw governance and ensured discipline in execution. This model kept domain understanding, technology development and operational feasibility aligned throughout.

We invested significantly in capability development. Laboratory teams were trained on middleware rules, digital triggers and Quality Control (QC) analytics. 

Customer-facing teams were upskilled on AI interaction guidelines, CRM usage and omnichannel workflows. Engineering teams received training in API automation, low-code development and cloud integration, while data teams were equipped with skills in analytics, model monitoring and clinical AI validation. 

These efforts have strengthened our internal capability for future digital initiatives

Which vendors were selected and why?

We adopted a combination of enterprise-grade platforms and specialised niche partners. Vendor selection focused on scalability, API readiness, healthcare domain expertise, interoperability with LIMS, CRM and ERP systems and strong data security and DPDPA compliance readiness. Their ability to support 24×7 distributed operations and align with our long-term digital roadmap was also critical.

Our approach balanced building and buying. We built internally in areas that are core differentiators for us, such as the consumer app, Metrobot enhancements and internal AI models. 

We chose to buy or partner in areas where specialised capabilities and speed-to-market were essential, including call AI, CRM enhancements and middleware platforms. Time-to-market, integration readiness and long-term ownership costs guided these decisions.

What are the top digital priorities for the next half-year?

In H2 FY26, our focus will be on expanding AI-driven initiatives, implementing pre-printed barcodes, enhancing digital dashboards, strengthening data protection, increasing automation across workflows and driving standardisation across recently acquired entities.