Optimising Hospital Operations: Amit Gandhi, CEO and Founder, Insight Tribe and Dr Amit Talele, Management Consultant

guest-article

IMT News Desk

The increasing competitive intensity across the healthcare landscape has made the adoption of a data-driven approach a necessity. Leveraging data and AI allows hospitals to rapidly streamline workflows, enhance patient care, and improve financial health.

There is an increasing focus on driving hospital efficiency; and rightly so. Efficient hospital operations are essential for timely and effective healthcare delivery. For patients, operational inefficiencies lead to delayed care and reduced access to critical resources; whereas for the hospitals, it translates into wasted resources, increased costs, and staff burnout.

Essentially, operational efficiency improves care delivery, minimises costs, and creates a better environment for staff and patients.

Key benefits include:

● Improved Patient Outcomes: Timely and accurate care reduces complications and recovery times.

● Enhanced Resource Utilisation: Streamlined workflows ensure optimal use of beds, staff, and equipment.

● Staff Well-being: Efficient systems reduce burnout and improve job satisfaction for healthcare workers.

● Financial Health: Reduced wastage and improved throughput increase revenue while controlling costs.

Timely care and better outcomes help build a hospital’s reputation as a trusted provider, attracting more patients and partnerships.

Key areas for improvement

While hospitals must strive to achieve enterprise-wide efficiency, some of the critical areas for prioritisation of efforts are:

● Patient Flow: Effective management of patient movement from admission to discharge touches upon a wide range of patient-facing and back-office functions; preventing bottlenecks, to deliver a superior patient experience and improving inpatient throughput capacity.

● Supply chain: An optimised supply chain not only ensures timely access to medications when required; but also minimises inventory levels and overall costs of buying and stocking medicines and consumables.

● Imaging diagnostic throughput: Efforts to streamline imaging diagnostics help ensure timely diagnosis and care. It is also critical from a hospital's perspective considering the higher contribution of imaging services to the hospital's profitability and that long waiting times or waitlists are known to be one of the factors driving patients to seek these services at stand-alone centres.

● Coordination of care: Smooth coordination of interdepartmental and interfacility care is crucial for delivering high-quality care and optimising utilisation of clinical resources.

● Overall Capacity Management: Balancing patient demand with available resources, such as beds and staff, is vital for ensuring patients have access to services within a reasonable time frame; in turn ensuring patients do not seek care elsewhere.

—-------------------------------------------------------------------------------------------------------------

A data-driven approach to optimisation

Data-driven strategies are key to identifying inefficiencies and implementing targeted improvements. By leveraging data, hospitals can make informed decisions to enhance operations.

Hospitals looking to adopt a data-driven approach to improve efficiencies, need to consider:

● Starting with establishing a Centralised Data System: Secure, centralised repositories make operational data accessible to authorised staff across departments, giving a unified view of patient flow for coordinated action across departments.

● Automation of Workflows: Automating routine processes reduces errors and accelerates patient management. Predefined care pathways, can be considered for automation to ensure standardised treatment and optimise the use of available resources.

● Use of Predictive Analytics: Analyzing historical and real-time data helps hospitals forecast demand, optimise scheduling, and anticipate resource needs.

By adopting the above, hospitals can proactively address challenges and enhance care delivery. Unified data is also the first step towards the use of artificial intelligence tools for the operations management of hospitals.

The Role of AI in Hospital Operations

Globally, there are multiple examples of how Artificial Intelligence (AI) is revolutionising hospital workflows, enabling smarter resource allocation, enhancing diagnostics and improving overall operating efficiency.

Some of the current applications of AI include:

Radiology Optimisation: AI-powered imaging tools improve diagnostic precision by detecting subtle abnormalities, reducing the workload on radiologists and ensuring timely interventions.
Tools like Oxipit’s ChestEye, an AI double-reading medical imaging solution, analyse final radiologist reports and medical images in near real-time. By identifying reporting errors, ChestEye helps to improve patient outcomes and reduce diagnostic inconsistencies.
Predictive Resource Allocation: AI anticipates patient admissions and resource demands, enabling hospitals to prepare for peak periods and avoid shortages.
Administrative Support: AI-driven chatbots and virtual assistants manage routine tasks like scheduling, record updates, and patient inquiries, freeing staff for direct patient care.
Preventive Care: AI analyses genetic, medical, and lifestyle data to identify at-risk patients, allowing for early interventions and customised preventive plans.
Companies like Nabla also utilise AI-powered tools with natural language processing (NLP) to streamline clinical workflows, enhance patient communication, and improve care quality.

These solutions exemplify how AI can directly enhance operational efficiency and outcomes.

—------------------------------------------------------------------------------------------------------------

AI-driven chatbots and virtual assistants manage routine tasks like scheduling, record updates, and patient inquiries, freeing staff for direct patient care.

—-------------------------------------------------------------------------------------------------------------

Becoming an insight-driven organisation

Transitioning to a data-intelligent model is essential for leveraging the wealth of information generated in hospitals. This transformation involves technological upgrades and cultural shifts.

Steps to becoming a data-driven healthcare organisation include:

Define Goals: Identify specific objectives, such as improving patient flow or reducing costs.
Evaluate Data Usage: Assess the organisation’s current data availability, and utilisation and identify gaps.
Identify Relevant Data Sources: Focus on data that aligns with operational goals, such as patient records or operational metrics.
Integrate Data Systems: Eliminate silos and ensure data from all sources is accurate, consistent, and accessible.
Encourage an Insight-driven Culture: Foster collaboration across departments and provide staff with training on analytics tools.
Implement Advanced Analytics: Use predictive tools to forecast trends and optimise decision-making.
Designated AI adoption team: Identify a team of relevant people from multiple functions who will work on the above, with progress being guided and directly monitored by the hospital's top-management team.

Adoption of a data-driven approach to management and use of AI tools requires a clear strategy and the right tools, but the benefits far outweigh the investment, enabling hospitals to remain competitive while improving care quality.

The outlook

The transition to a data-driven organisation requires investment and commitment but promises significant returns. Hospitals that embrace these strategies will be well-positioned to meet modern healthcare challenges and deliver exceptional care to their communities. Efficiency is not just about cost-saving—it is about ensuring timely, accurate, and compassionate care for every patient.