Using Analytics to Increase Staffing Productivity and Improve Hospital Operations

Below is the transcript of a webinar hosted by InetSoft on the topic of Using Analytics to Increase Staffing Productivity and Improve Hospital Operations. The presenter is Abhishek Gupta, Chief Data Scientist at InetSoft.

Creating a Data Management Team

My data management team was created five years ago. The vision is to simplify technology for a dynamic success. In this five year journey, we will share with you how we did this in the five years. So now we are in our ninth year in this journey. When we started out in our journey in the first year, because the needs are tremendous, we realized that we needed to maximize and develop availability of the information right away. In doing that, I would share with you in the next few slides.

We are the data management team. Today we are confident that we have achieved what we set out to do. We applied the right technologies. We developed the appropriate skills, and we hired the right people to take us on this journey. Today my team can take on any project, any size of data and can transform the data into valuable information to support the daily operations.

The team is comprised of 14 people, 14 fulltime and 25 project coordinators, and the director of the team is myself. We have one IS Project Manager, one lead PI consultant. She is Lean Six Sigma Blackbelt Certified as well as a nurse. We have five software engineers and one data scientist. We do have two application developers and two database analysts. All of us are strong in Computer Science background Mathematics, Data Science Analytics and Predictive Analytics.

The nurse who has joined our team, she has background in Nurse Educator PI, Performance Improvement as well as Lean Six Sigma. We come from six countries, speak nine different languages, and have a total of 195 years of experience. Our approach is very simple: if it has no value for the business, don't do it. The business we are in is we focus on taking care of the patient. Translating to IT, into data management, we provide the information timely to the people who care for the patients so that they can make the right decision, right time and the right place.

Reuse and Share Resources

The second thing in our approach that we found useful throughout the years is that we support an integrated system to reuse and share resources as much as possible in an effective way. In the next few slides we will share with you how we do that and focus on two things. If it has no value to the business, don't do it. And also integrate data in ways that you can reuse the data. Let's talk a little bit about this approach.

On the screen you see the model that we used. Many people out there already use this model, but five years ago, very seldom we have seen this model. As you know business users discover insights buried deep in the data. The need for the data to support business decisions has grown faster than we can anticipate it. The challenge that faced us were the inability to analyze larger volumes of data. Instead we had summarized data which loses the detail and insight. There was a lack of connection between BI analytics and the needs of the business. And lastly the cost of adding capacity and licenses was prohibitive. Those are the challenges that we faced in the past.

The enterprise model approach in the past that we had here is where the data warehouse was designed from the top-down approach. In this model we tend to model a perfect database from the outset, from determining the events, everything we would like to be able to analyze, and then we structure the data accordingly. In many years in the healthcare analytics, I have never seen a project using this approach generating great results over two years of effort. The delay of time to value is a significant downside of this model. Integrating data and defining every possible business rule and events takes a lot of time.

Hospitals extending their reporting capabilities increasingly focus on unifying clinical, operational, and financial data into a single analytical environment. When these domains remain siloed, leaders struggle to understand how staffing levels affect patient throughput, how clinical outcomes influence reimbursement, or how supply chain delays impact care quality. A modern reporting solution brings these datasets together, allowing administrators to see the full picture and make decisions based on integrated evidence rather than isolated metrics. This unified approach strengthens enterprise-wide hospital data visibility and supports more coordinated decision-making.

Another important enhancement involves improving the timeliness of clinical reporting. Traditional hospital reports often rely on batch processes that update nightly or weekly, leaving clinicians and managers with outdated information. By adopting real-time or near-real-time pipelines, hospitals can surface changes in patient acuity, bed availability, or procedure volumes as they occur. This immediacy enables faster interventions, reduces bottlenecks, and improves patient flow. As organizations modernize their infrastructure, real-time reporting becomes a cornerstone of clinical operations intelligence.

Hospitals also benefit from expanding reporting beyond compliance and regulatory needs. While mandatory submissions—such as quality measures, infection rates, and readmission statistics—remain essential, advanced reporting tools allow organizations to explore deeper operational questions. For example, leaders can analyze correlations between staffing patterns and patient satisfaction, or evaluate how equipment utilization affects procedure turnaround times. These insights help hospitals shift from reactive compliance reporting to proactive performance improvement supported by operational analytics.

Another valuable extension is embedding predictive analytics directly into hospital reporting workflows. Instead of simply showing historical trends, reports can highlight emerging risks such as rising patient deterioration indicators, expected surges in emergency department volume, or projected shortages in critical supplies. Predictive models integrated into dashboards help clinicians and administrators act before issues escalate, improving both care quality and resource management. This evolution transforms reporting from a retrospective tool into a forward-looking engine for predictive hospital management.

Finally, hospitals can strengthen their reporting strategy by improving accessibility for non-technical users. Many clinicians and department managers lack the time or training to navigate complex BI interfaces. A modern reporting solution provides intuitive dashboards, guided navigation, and role-based views tailored to each user’s responsibilities. When insights are easy to access and interpret, adoption increases and data-driven decision-making becomes part of everyday practice. Over time, this usability focus helps build a culture of clinical data empowerment across the entire organization.