This on-demand hospital staffing dashboard tool shows six shifts, six roles, and status indicators colored red or green including a button to send the result to management to ask, why am I in red? The manager's feedback is really amazing. They loved the tool. They find it easy to use. That is the goal, to use the tool.
We aim for 80% of the staff to use it, but many units have used it almost close to 100%. Many reports have been developed. So far we have about 10 reports that come with the system. The first one would be the unit weekly staffing report. It has detail with information from the employee level where and how they punched in, when they punched in, and how many hours and minutes they worked. Variances generally are displayed right on top of the screen, and on the bottom of the screen will be, who, when, where and how many hours.
This information definitely is a must have. As managers come forward and wonder why I am over budget, we provide detailed information of just fix staffing, variable staffing, premium pay, overtime or even early in, late out or cancel bill. All of that information is available on this quick little report on the screen.
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Weekly Summarized Report of Prior Weeks' Performance
Directors get a weekly summarized report of prior weeks' performance, and it is very well received. Directors asked us to create this report so they can see an overall picture of all the units that they have. They can also drill into a particular shift and date and the reasons why there are variances. On the right-hand side you can see the reason why particular units are over or under staffing.
For implementation and training for this project, we use the same team that we have. We built an information access product and we implement the same product to our users, over 100 managers and assistant nurse managers. We provide security and access to the product and weekly we send our weekly updates, new tools and new functions and new changes in addition to fixes.
In the journey we have ran, we prepared the data. For this step you must have a way to manage large data sets. The data is in raw form. It could be millions and billions of rows. At this stage we must have a way to handle large amounts of data and be able to put InetSoft right on top of it. The one thing that we don't do in our team is that we don't extract data into InetSoft. We place the data intelligence application directly on top of our Vertica database engine so that it saves time as well as resources.
We also have report to our management the performance of 55 inpatients units as well as over 400 departments at the hospital. At the end of each payroll period, we know exactly where we are in total labor and staffing. At the current stage, my team has integrated all the data that we need to support our hospital.
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We are ready to go into the next stage which is leaping into the future, and my team is so excited about this stage right now. Last year when we acquired Micro Focus to use with Vertica and InetSoft, we were able to pilot a new hospital analytics project on sepsis cases. We used the tools not to just the generate the report but to produce the outcome and hand those sepsis bundles for investigation. We also test a new project that involve a very big data set, 15 billion rows of data that have for all charges and all patients for business decision support in the entire hospital system. The two projects that we work on handle Big Data and use an AI solution which I will discuss.
How AI Can Be Used in a Hospital
Artificial intelligence (AI) holds immense potential to revolutionize healthcare delivery and improve patient outcomes, making hospitals more efficient, accurate, and responsive to patient needs. One significant application of AI in hospitals is in medical imaging analysis. AI-powered algorithms can analyze medical images such as X-rays, MRIs, and CT scans with remarkable speed and accuracy, assisting radiologists in detecting abnormalities and diagnosing diseases earlier. By leveraging deep learning techniques, AI can identify subtle patterns and anomalies in medical images that may be missed by human observers, leading to more timely and accurate diagnoses and treatment recommendations. Another important use of AI in hospitals is in predictive analytics for patient care. By analyzing electronic health records (EHRs), vital signs, and other clinical data, AI algorithms can predict patient outcomes, identify individuals at risk of developing complications, and personalize treatment plans accordingly. For example, AI-powered predictive models can help healthcare providers anticipate deteriorations in patient condition, allowing for early interventions to prevent adverse events such as sepsis or cardiac arrest.
Additionally, AI can optimize hospital workflows by predicting patient admission rates, bed occupancy levels, and resource utilization, enabling better resource allocation and capacity planning to meet patient demand more effectively. Furthermore, AI-enabled virtual assistants and chatbots are increasingly being used in hospitals to enhance patient engagement and streamline administrative tasks. These virtual agents can provide patients with personalized health information, answer frequently asked questions, schedule appointments, and facilitate medication reminders, thereby improving patient satisfaction and reducing the burden on hospital staff. By leveraging natural language processing (NLP) and machine learning algorithms, AI-driven virtual assistants can understand and respond to patient inquiries in real-time, offering a seamless and efficient communication channel between patients and healthcare providers. Overall, AI has the potential to transform every aspect of hospital operations, from diagnosis and treatment to patient care and administrative tasks, ultimately leading to more efficient, effective, and patient-centered healthcare delivery.