POKE-R – Using Analytics to Reduce Patient Harm
Major events and surgeries are not the only sources of trauma during a hospital encounter. Many small, less invasive events such as shots, line placements, blood draws, and imaging studies happen throughout a patient’s hospital stay.
Many of these less traumatic events have the potential to negatively impact patient outcomes by increasing the risk of hospital-acquired infections through skin invasions and exposure to organisms, reducing the patient experience by causing pain and frustration, increasing cost and causing other complications. The goal of this project is to reduce such events when they are not clinically required.
This is an analytics project so this goal is facilitated by making accurate and meaningful information available to the appropriate personnel. This includes timely information to clinicians so they can alter treatment, and retrospective trend analysis to enable and track performance improvement and identify opportunities for additional process improvement.
Improving Patient Care Through Analytics
Use of advanced electronic health record (EHR) systems has grown rapidly in the United States.
This has created an abundance of data previously unavailable for analysis. Many health organizations now have reporting systems for operational key performance indicators (KPIs) and regulatory metrics and data warehouse systems for analytics. However, using this increasing information as meaningful knowledge to increase quality of care remains a challenge.
This paper provides our experience utilizing an enterprise data warehouse and business intelligence tools to improve clinical outcomes for patients.
The Virtual Enterprise Data Warehouse For Healthcare
Healthcare organizations have access to more data than ever before. Healthcare analytics is a vital tool for healthcare organizations and hospitals to analyze performance, identify opportunities to improve, make informed decisions, and comply with government and payor regulations.
However, the field of medicine and the political and regulatory landscape are constantly changing, thus these requirements and opportunities rapidly evolve. The traditional best practice solution for business analytics is to organize and consolidate the data into a dimensional data warehouse for analytics purposes. Due to the size of the data, the number of disparate sources and the volume of analytics needs, the overhead to create and maintain such a data warehouse is becoming prohibitive.
In this paper, we introduce a virtual data warehouse solution that combines the design and modelling principles of traditional dimensional modelling with data virtualization and in-memory database architectures to create a system which is more agile, flexible and scalable.
Managing Evolving Code Sets and Integration of Multiple Data Sources in Health Care Analytics
This paper presents our industry experience related to developing data warehouses for healthcare analytics. With the rapid advancement of medical record digitization, there is a very large amount of information available for analysis.
With the heavy focus on driving down health care costs, managing preventive care and improving patient outcomes and satisfaction, there is a growing emphasis on healthcare metrics and analytics. The information for a single patient’s history is composed of data from every hospital, provider, lab, pharmacy and insurance company the patient has encountered. This information needs to be viewed as a whole to accurately analyze the patient’s health. In turn, each patient’s complete health information is needed to accurately evaluate the performance of his or her providers.
This paper will address some challenges we have faced when merging and correlating these diverse data sources. We will provide our solutions and experience addressing key challenges including code set integration and migration and patient identification.
AMIA POKE-R - Using Analytics to Reduce Patient Harm
Every time a patient’s skin is broken, the opportunity increases for hospital-acquired infections. Every time blood is drawn the potential for anemia is increased. Each time a radiology imaging study is performed, the patient is exposed to radiation. There is significant literature demonstrating the harmful effects of all of these events on the patient’s health.4 5 6 Many of these events are not clinically necessary and increase costs while reducing patient outcomes. This is especially true for pediatric patients. This problem and proposal is first described by Dixie Regional Medical Center1 . We have used analytics in conjunction with structured rounding to implement the proposed process, and have added radiology to the types of events considered.
Health INF 2018 - Predicting Hospital Capacity and Efficiency
Hospitals and healthcare systems are challenged to service the growing healthcare needs of the population with limited resources and tightly restrained finances. The best healthcare organizations constantly seek performance improvement by adjusting both resources and processes. However, there are endless options and possibilities for how to invest and adapt, and it is a formidable challenge to choose the right ones. The challenge is that each potential change can have far reaching effects. This challenge is exacerbated even further because it can be very expensive for a hospital to experience logjams in patient movement. Each and every change has a “ripple” effect across the system and traditional analytics cannot calculate all the ramifications and opportunities associated with such changes. This project uses historical records of patient treatment plans in combination with a virtual discrete event simulation model to evaluate and predict capacity and efficiency when resources are added, reduced or reallocated. The model assigns assets as needed to execute the treatment plan, and calculates resulting volumes, length of stay, wait times, cost. This provides a valuable resource to operations management and allows the hospital to invest and allocate resources in ways that maximize financial benefit and quality of patient care.