AI Business Use Case in Healthcare - Operation Inefficiency

Author: Erfan[email protected]
Publish on: 2023-10-08
A Thorough Investigation into how AI Technologies are Being Employed to Streamline Various Operational Processes within Healthcare Facilities.
Blog Pic AI Business Use Case in Healthcare - Operation Inefficiency

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In recent years, Artificial Intelligence (AI) has emerged as a crucial tool to address operational inefficiencies in the healthcare sector. The complexities arising from fluctuating team schedules and the challenge of maintaining up-to-date communication within large and dispersed healthcare teams often result in time-consuming managerial tasks. However, AI and digital tools are now aiding in streamlining these operational processes. For instance, solutions like Microsoft 365 Copilot have been developed to assist care team managers in quickly identifying and addressing open items such as open shifts and time-specific work, thereby enhancing operational efficiency.

Furthermore, AI's role extends to improving critical processes and workflows, which is fundamental for boosting operational efficiency. By focusing on consistency and addressing critical processes first, a robust foundation for further enhancements is established. AI technologies help in identifying areas that require immediate attention and improvement, making operational efficiency improvements scalable not only to individual healthcare providers but also to larger healthcare organizations. Moreover, the mitigation of challenges such as clinician burnout and low performance through AI support in clinical processes and operational workflows significantly contributes to enhanced service quality and patient satisfaction.

The momentum for AI adoption in healthcare is notably on the rise, with a growing number of healthcare executives recognizing the potential of AI in transforming operational dynamics. A survey by health insurer Optum revealed that 85% of healthcare executives now have an AI strategy, and nearly half of them are actively utilizing this technology. This upward trend in AI adoption is expected to induce a substantial shift in healthcare operations, making AI an indispensable asset in the quest for operational efficiency. As AI continues to evolve, its application is anticipated to expand further, promising a future where operational inefficiencies in healthcare are significantly reduced, leading to more insightful and effective care and operations.

Historical Context

Before the advent of AI, healthcare operations dealt with inefficiencies through various conventional methods which, though effective to an extent, had numerous shortcomings.

1. Software Usage

Healthcare organizations relied on a multitude of software solutions to manage their operations. A survey revealed that nearly 60% of respondents used more than 50 unique software solutions for various healthcare operations functions such as workforce management, provider data management, contract and spend for supply chain, and facility access among others.

2. Operational Inefficiencies and Costs

Operational inefficiencies were a significant concern, as they could adversely affect both clinical outcomes and financial performance. These inefficiencies represented a massive opportunity for healthcare organizations to increase their retained earnings while maintaining the same revenue levels. Moreover, operational failures had both clinical and financial costs, directly impacting patient health and well-being.

3. Inventory Management

Over-procuring inventory was a common operational inefficiency. Healthcare providers often had to manage inventories of one-time-use products like syringes and medications, and multi-use products like medical devices and reusable equipment. The lack of precise demand forecasting led to either overstocking or understocking, both of which had financial implications.

4. Asset Mismanagement

The loss of inventory, especially medical devices, was another significant source of inefficiency. Medical devices often got misplaced when moved to areas outside their usual storage places, leading to financial waste. Asset management programs were developed to digitally track each medical device in a facility to monitor their location and movements, aiming to improve visibility and informed inventory planning.

5. Unnecessary Transportation

The unnecessary movement of medications, supplies, or devices was identified as a source of waste. Any transportation of these items meant they were unavailable for use during the transition, affecting operational efficiency. The movement of devices from one care area to another could decrease the time the device was in use and productive, contributing to operational inefficiencies.

###6. Supply Chain Disruptions and Data Management Challenges such as supply chain disruptions, data management, and interoperability were difficult to manage, affecting the operational efficiency and resilience of healthcare organizations.

7. Improvement Initiative

Efforts were made to reduce operational inefficiencies by adopting lean healthcare models, reducing inventory levels to avoid unnecessary surpluses, and implementing asset management programs to better manage medical devices and other assets.

These traditional methods and challenges highlight the complexity and multifaceted nature of operational inefficiencies in healthcare, establishing a clear need for more intelligent, automated, and real-time solutions that AI technologies can potentially provide.

Advent of AI

AI has emerged as a potent solution to tackle operational inefficiencies in healthcare, delivering significant improvements in various operational domains. Here are some ways AI has helped overcome these problems:

1. Automating Routine Processes

AI is already automating many of healthcare’s most costly, high-volume routine processes, which used to consume a significant amount of time and resources. Through advancements like machine learning and computer vision, the potential for applications across operational AI has dramatically increased.

2. Enhancing Supply Chain Management

Operational redundancies and inaccurate supply chain forecasting were common issues in healthcare operations. AI has enabled faster decision-making and more accurate inventory management, which in turn increases flexibility and reduces healthcare costs. By reducing operational redundancies, healthcare facilities can optimize their supply chains, ensuring that resources are utilized efficiently and waste is minimized.

3. Data-Driven Decision Making

Hospital leaders have turned to data and analytics to overcome operational inefficiencies. AI, with its advanced analytics capabilities, provides insights that help in making informed decisions to improve efficiency. By analyzing vast amounts of data, AI can identify patterns and provide recommendations for optimizing operations.

4. Addressing Clinical Demand

AI helps in addressing unmet clinical demand, which is a significant operational challenge in healthcare. Effective healthcare operations and optimal clinical outcomes rely heavily on a series of complex activities involving multiple care paths and interdependent workflows across various departments. AI has the potential to streamline these workflows, ensuring that resources are allocated efficiently to meet clinical demand.

5. Improving Clinical Workflows

The introduction of AI-infused solutions like Conversational AI, Ambient AI, and Generative AI has propelled the healthcare industry forward by solving workflow challenges. These AI advancements enable new functionalities, improve patient care, and enhance clinician-patient experiences. By automating routine tasks and streamlining workflows, AI allows healthcare professionals to focus more on patient care and less on administrative tasks.

These advancements depict a transformative phase in healthcare operations, where AI is not only tackling existing operational inefficiencies but also paving the way for novel solutions that enhance operational efficiency and patient care. Through automation, data analytics, and intelligent workflow management, AI is significantly reducing the burden of operational challenges in healthcare, promising a more efficient and responsive healthcare system.

Real Life Application AI to Operation Inefficiency

1. Cognizant Case Study

A US-based healthcare company that provides revenue cycle management solutions aimed to utilize organizational data more effectively and sought to automate the discovery of insights from its extensive data repositories. Cognizant developed a smart business operational assistant called RESOLV using Microsoft’s Azure AI platform, which facilitated real-time business analytical queries using natural language processing. This implementation resulted in 45% faster provider decisions, with the tool auto-generating reports and handling queries on patient responsibility, coding, and billing, thus saving 88% of manual effort.

2. McKinsey & Company

A report from McKinsey & Company mentions case studies on the impact of automation and AI on healthcare, although the detailed case studies were not accessible due to a technical issue. The report likely covers how AI and automation are transforming healthcare operations.

3. Deloitte Insights

Deloitte Insights has organized global case studies on digital health technology, which might include examples of AI improving operational efficiency in healthcare. The case studies are organized based on the future of health themes they represent.

4. Olive AI

Olive AI lists operational applications for healthcare AI such as automating eligibility checks and prior authorizations. By employing robotic process automation (RPA), computer vision (CV), and machine learning (ML), AI can automate many processes that previously bogged down healthcare operations.

5. Oracle

Oracle discusses the benefits of using AI for financial needs and operations in healthcare organizations. It mentions how AI can be used for root cause analysis of issues, predictive analytics on organizational trends, and modeling to optimize processes, resources, and supply chain needs.

6. NEJM Catalyst

A summary on NEJM Catalyst discusses how healthcare providers and staff are spending an increasing amount of time on administrative tasks, and it hints at how AI could enhance the management of administrative tasks in healthcare, although the specific applications and examples were not detailed in the quoted text.

Future Trend

The future of operational inefficiency in healthcare seems promising with the advent of various technologies. Here's a breakdown of the trends and insights gathered from different sources:

1. Technological Advancements:

The healthcare sector is heading towards a more technologically advanced future where AI, machine learning, and digitalization play pivotal roles in improving operational efficiency. The transition towards more digital operations is expected to streamline processes, reduce administrative burdens, and facilitate better decision-making, thereby addressing operational inefficiencies.

2. Automation:

Workforce shortages, rising workloads, and economic pressures are driving the need for improved operational efficiencies. Workflow automation is expected to address workforce shortages and significantly contribute to operational efficiency. By automating routine and mundane tasks, healthcare providers can allocate resources more efficiently, improve patient care, and reduce operational costs.

3. Supply Chain Management:

As per a report, hospitals are focusing on addressing cost and supply chain challenges as part of their operational efficiency objectives for 2023. By optimizing the supply chain through AI and data analytics, healthcare institutions can enhance supply reliability, reduce costs, and improve operational efficiency.

4. Innovative Care Delivery Models:

The evolution of care delivery models is another trend that is expected to impact operational efficiency positively. Through innovative care delivery models, healthcare institutions can provide better care at reduced costs, thereby improving operational efficiency. The integration of AI and other technologies will play a crucial role in developing and implementing these innovative care delivery models.

5. Data Utilization:

Utilizing data effectively is key to improving operational efficiency. With the help of AI and machine learning, healthcare institutions can analyze vast amounts of data to identify inefficiencies, make informed decisions, and improve operational processes.

6. Global Health Policies and Emerging Threats:The future also holds a focus on international health policies and identifying emerging threats that could impact operational efficiency. Being prepared and adapting to these changes early could significantly benefit healthcare institutions in maintaining or improving operational efficiency.

These trends signify a move towards a more efficient and technologically driven healthcare sector, where operational inefficiencies are addressed through digitalization, automation, innovative care delivery models, effective data utilization, and a proactive approach towards global health challenges.


As we navigate through the era of digital transformation, the healthcare sector stands on the cusp of significant change. The historical hurdles of operational inefficiency that once seemed insurmountable are being addressed head-on with the aid of Artificial Intelligence and innovative technologies. Real-world case studies have illustrated the profound impact AI is already having on operational workflows, supply chain management, and data-driven decision-making. The momentum for AI adoption continues to build, with healthcare executives increasingly recognizing the indispensable value of AI in enhancing operational efficiency. The trends for 2023 further underscore this narrative, showcasing a trajectory of continuous improvement powered by digitalization, automation, and innovative care delivery models.

Looking ahead, the fusion of AI with other emerging technologies and progressive care delivery models will likely usher in a new era of operational excellence in healthcare. The potential to not only rectify existing inefficiencies but also preemptively address challenges through predictive analytics and proactive management is transformative. As healthcare providers worldwide grapple with workforce shortages, mounting workloads, and economic pressures, the imperative for operational efficiency becomes ever more critical. The journey towards operational efficiency is a multifaceted endeavor, demanding a harmonious blend of technological innovation, strategic insight, and a steadfast commitment to delivering superior patient care. Through the lens of AI, the future of operational efficiency in healthcare is not just promising; it's pivotal. The burgeoning synergy between AI and healthcare operations heralds a future where enhanced operational efficiency is synonymous with improved patient care and robust healthcare systems ready to meet the demands of the 21st century.

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