Harnessing the Power of Data Analytics for Improved Patient Outcomes
The healthcare industry is increasingly utilizing big data analytics, advancing from basic descriptive analytics to predictive insights. Predictive analytics provide information on potential future outcomes based on historical data, enabling financial, medical, and administrative staff to make more informed decisions. Predictive analytics is particularly beneficial in emergency care but is also useful in areas such as financial administration and data security. Healthcare businesses are deploying analytics throughout the enterprise to extract actionable insights and improve healthcare for patients and reduce costs.
Digitization, big data use, and data analytics can help organizations in the healthcare industry, such as physician offices, hospital networks, and accountable care organizations gain many benefits. These include early disease detection and treatment, population health management, and fraud detection. McKinsey estimates that the U.S. healthcare industry could save over $300 billion per year by reducing expenditures by 8%.
30-day hospital readmission avoidance
Hospitals and other health organizations have to deal with significant penalties under Medicare's Hospital Readmissions Reduction Program (HRRP). These include financial incentives for preventing unplanned returns to the inpatient setting. Luckily, data analytics is capable of not only improving transitions of care and deploying care coordination strategies but also warning the providers when a patient's risk factors indicate a high chance for readmission within the 30-day window.
A study conducted by the University of Texas Southwestern in 2016 discovered that certain events occurring during a hospital stay, such as vital sign instability upon discharge, C. difficile infection, and overall extended length of stay, lead to a significantly raised chance of 30-day readmission.
Analytical tools with the capability to assess patients with traits that produce a high impact on the chances of readmission can offer providers extra indications of when to concentrate resources on follow-up and how to create discharge planning protocols to prevent potential returns to the hospital.
During their stay in the hospital, patients will have to deal with various potential threats to their wellbeing, such as the development of sepsis, the acquisition of a difficult infection, or a sudden downturn because of their current clinical conditions. Data analytics will be capable of providing a faster way to respond to the changes in a patient's vitals and can identify an upcoming deterioration before symptoms start becoming more transparent. Machine learning techniques are becoming very well-suited for predicting clinical situations in the hospital, such as the potential development of sepsis.
The University of Pennsylvania managed to use an analytic tool to leverage machine learning and EHR data, which helped them identify patients on the pathway towards severe sepsis or septic shock 12 hours before the onset of the condition, as shown in the study done in 2017.
Another initiative conducted at the Huntsville Hospital in Alabama discovered that by combining analytics and clinical decision support (CDS) tools, they could reduce sepsis mortality by more than half. This analytics-driven strategy manages to far exceed the accuracy of any existing premium tool.
Preventing Appointment No-Shows
Expected schedule changes can lead to financial ramifications for the organization while disrupting the workflow of clinicians. Relying on analytics to identify patients who are most likely to skip appointments without prior notice can significantly improve providers, prevent revenue losses, and allow the organization to offer open slots to other patients, thereby enhancing the response to care.
Electronic health record (EHR) data can reveal individuals with the highest likelihood of not showing up. A study by Duke University showed that predictive models using clinic-level data could accurately identify an additional 4800 patient no-shows per year, compared to previously attempted forecasts. Providers can use this information to send additional reminders to at-risk patients, offer transportation or other services to help them attend their appointment, or suggest alternative settings and times that may better suit them.
Managing the Supply Chain
A supply chain can become the most costly center for providers, representing the most significant opportunity for those in the health industry to trim unnecessary expenditures and improve efficiency. Analytical tools are increasingly in demand among hospital administrations seeking to reduce variations and gain practical insights into orders and supply usage. Around 17 percent of hospitals are actively using automated or data-driven solutions to manage their supply chains. Global Healthcare Exchange has listed analytical tools for supply chain management as the number one item on the wish list, followed by a survey in 2018 that found adopting data analytics tools to be the highest priority.
Relying on analytics tools to monitor the supply chain and make proactive, data-driven decisions about spending can potentially save hospitals an estimated $10 million per year. Descriptive and predictive analytics can support decisions to negotiate pricing, reduce variation in supplies, and optimize the order process.
Analytical tools, along with artificial intelligence, are expected to play a vital role in cybersecurity, especially as the sophistication behind cyber attacks continues to expand.
Utilizing analytical tools to monitor patterns in data access, sharing, and utilization can allow any healthcare organization to receive a warning when something is about to change, especially if those changes specify that an intruder possibly entered the network. According to David McNeely, predictive analytic tools and machine learning strategies can potentially calculate real-time risk scores for specific transactions or requests and respond accordingly depending on how the event has been noted down.
Prevention of suicide and self-harm
Early assessment of a patient who is most likely to cause harm to themselves can ensure that these patients receive the appropriate mental health care required to avoid serious situations, such as suicide. According to Kaiser Permanente, EHRs have the largest amount of data to support suicide risk detection. During a study conducted by KP and the Mental Health Research Network in 2018, the usage of both EHR data and a standard depression questionnaire accurately identified people who had elevated chances of committing suicide.
With the help of a predictive algorithm, the team managed to discover that suicide attempts and successes were 200 times more likely among the top 1 percent of patients flagged. The strongest prediction behind self-harm attempts included mental health or substance abuse diagnoses, previously attempted suicide, the usage of any psychiatric medications, and high scores on a questionnaire about depression.
Developing medicine and therapies
Providers and researchers are also utilizing analytics to supplement traditional clinical trials and drug discovery techniques. In silico testing is a method that has the potential to reduce the necessity of recruiting patients for complex and expensive clinical trials while increasing the evaluation of new therapies.
According to FDA Commissioner Scott Gottlieb, MD, the FDA's Center for Drug Evaluation and Research (CDER) is currently utilizing modeling and simulation to predict any clinical outcomes, inform clinical trial designs, support evidence of effectiveness, optimize dosing, predict product safety, and evaluate potential adverse event mechanisms.
In silico models are being utilized to develop control groups for these trials related to degenerative conditions such as Parkinson’s disease, Huntington’s disease, and Alzheimer’s.
Predictive analytics and clinical decision support tools are also playing a major role in translating new drugs and precision therapies. CDS systems are beginning to be capable of predicting a patient’s response to a course of treatment by matching genetic information within the results from the previous patient, allowing the provider to decide on the best therapy with the most likely chance to succeed. Doing this can improve the outcomes and allow researchers to increase their understanding of relationships between a genetic variant and the effectiveness of certain kinds of therapies.
Improving Patient Engagement and Satisfaction
Along with supporting chronic disease management strategies, lowering wait times, and targeting therapies to develop better outcomes, data analytics can keep patients engaged in other areas of their care as well. Consumer relationship management has increasingly become an important skill for both providers and insurance companies searching to promote wellness and reduce long-term spending. Being able to predict patient behaviors is the main ingredient for developing effective communications and adherence strategies.
Anthem is one of the many healthcare organizations using its data analytics tools to develop consumer profiles that allow the payer to send messages, increase customer retention, and assess which strategies are most likely to be impactful for each potential customer.
Providers are also using behavioral patterns to develop a more meaningful care plan and keep patients active with their financial and clinical responsibilities. By utilizing analytics to inform care management decisions and develop a much stronger, more motivational relationship between patients and providers, engagement can be increased, and the risks connected to chronic diseases can be reduced.
Data analytics have the capability to improve the healthcare industry in all sorts of manners, with technology increasing each year, new and improved techniques are being discovered. If you wish to improve healthcare for patients and reduce the cost of your organization, consider becoming an adopter of these analytical tools and get ahead of the competition.