How Analytics Can Improve Healthcare
With the healthcare industry developing increasingly sophisticated big data analytics capabilities, the industry is starting to ascend from basic descriptive analytics towards the area of predictive insights. While predictive analytics is only one of the few paths towards growing the capabilities of analytics, it does represent a massive step forward for many businesses.
Instead of offering information about a person's history to the user, predictive analytics estimates the chances of future outcome based on the information garnered by the historical data. This allows the financial experts, medical staff, and administrative staff to receive alerts about potential situations before they occur, and therefore make more informed decisions about how to proceed with a choice.
Being a step ahead of a serious situation is vital and can be seen in the areas of intensive care, surgery, or emergency care, where a patient’s life may all come down to a rapid decision and finely tuned sense of when something has gone wrong.
Even so, large use cases of analytics exist throughout the healthcare industry and may not always deal with real-time alerts that require a team to immediately respond to the situation. Those in the payer and provider industry can apply analytical tools for their financial administration, and data security challenges, and witnessed significant amounts of efficiency and consumer satisfaction.
Now, how are businesses in the healthcare industry deploying analytics capabilities all over the enterprise to extract actionable insights, from their growing data assets? And how can they help you improve healthcare for patients and reduce costs?
Advantages
With digitization, mixing, and effectively using big data, any organization from the healthcare industry, such as single physician offices and multi-provider groups, large hospital networks and accountable care organizations stand to realize the benefits. The potential benefits for these include detecting disease during its early stages when they can be treated seamlessly; managing specific people and population health and detecting health care fraud more rapidly and efficiently. Plenty of these situations can be handled with the help of data analytics. Certain situations and outcomes can be predicted and estimated based on the amount of historical data gathered, such as length of stay. McKinsey has estimated that data analytics can allow more than $300 billion in savings per year in U.S. healthcare, with two-thirds of that through reduction by 8% in national health care expenditures.
30-day hospital readmission avoidance
Hospitals and other health organizations must 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 are capable of not only improving transitions of care and deploying care coordination strategies but warn the providers when a patient’s risk factors specify 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 a vital sign instability upon discharge, C. difficile infection, and overall extend the length of stay, lead toward 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.
Patient deterioration
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 manages 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.
Preempt appointment no-show
Expected schedule changes can lead toward financial ramifications for the organization while throwing the workflow of a clinician way off. By relying on analytics to identify patients most likely to skip out on an appointment without future notice can vastly improve providers, slash off any revenue losses, and give the organization the chance to offer open slots for other patients, thereby increasing the response to care.
EHR data will be able to reveal individuals who have the highest chance of not showing up. A study done by Duke University has revealed that predictive models utilizing clinic-level data could gather an additional 4800 patient no-show per year with increasing accuracy than previously attempted forecasts. Providers will be able to utilize this information to send additional reminders to patients at risk of not showing up, offer transportation or other services to help these individuals make it to their appointment, or suggest alternative settings and times that may suit them better.
Managing supply chain
A supply chain can become the provider's most costly centers and represents the most significant opportunity for those in the health industry to trim out any unnecessary expenditures and improve efficiency. Analytical tools are increasing in demand among hospital administrations looking to reduce variations and gain more practical insights into orders and supply usage. Around 17 percent of hospitals are actively using automated or data-driven solutions to handle their supply chains. Global Healthcare Exchange has listed analytical tools for supply chain management to be the number one sought-out item on the wish list, followed by a survey during 2018 showing that adopting data analytics tools remained the highest priority.
Relying on analytics tools to monitor the supply change and make proactive, data-driven decisions about spending can potentially save hospitals an estimated $10 million per year. Both descriptive and predictive analytics will be capable of supporting decisions to negotiate pricing, reduce the variation in supplies, and optimizing the order process.
Data security
Analytical tools along with artificial intelligence have been anticipated for having a major and vital role in cybersecurity, especially as the sophistication behind cyber-attacks continues to expand.
Utilizing analytics 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 can specify 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 most likely to cause harm to themselves can ensure that these patients receive the appropriate mental health care required to avoid some 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 manages 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 chance 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 a degenerative condition such as Parkinson’s disease, Huntington’s disease, and Alzheimer’s.
Predictive analytics and clinical decision support tools are having a major role in translating new drugs and precision therapies as well. CDS systems are beginning to be capable of predicting a patient’s response to a course treatment by matching genetic information within the results from the previous patient, allowing the provider to decide the best therapy with the most likely chance to succeed. Doing this can improve the outcomes and allow the 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 the 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 the provider 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 can increase long-term engagement and reduce the risks connected to chronic diseases.
Conclusion
Data analytics has the capability to improve the healthcare industry in all sorts of manner, 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 adopting analytical tools and get ahead of the competition.