Transforming Healthcare Analytics. Michael N. Lewis
them with better treatment so they don't have to encounter long-term health issues. This information minimizes long-term care, which could mean nominal costly treatments to alleviate complications that might arise.
Quality Patient Care – Stellar patient care is extremely important. Doctors need to evaluate the symptoms and quickly provide a course of action to pull in other doctors and/or nurses to execute. Analytics can positively impact quality care by analyzing all of the data points of the symptoms and prescribe actions to be carried out based on the diagnosis. Analytics answers the question of what to do, providing decision option(s) based on current and historical data points.
Proactive Patient Analysis – Clinicians often advise patients that there is a chance of infection when there is a procedure or operation conducted on the patient. Patients encounter a number of potential threats to their recovery or wellbeing while still in the hospital. These threats include the development of sepsis, hard-to-treat infection, or an unexpected downturn due to their existing medical condition. Data such as vitals from the patient can be analyzed and analytics can provide clinicians insight to changes in a patient's vitals and allow them proactively to identify relapse before severe symptoms manifest themselves that the naked eye cannot detect.
Operations Management – Analytics help with managing staffing needs and operations such as emergency care or intensive care departments. Having the right staffing in areas of quick response time can save lives. With improved technological infrastructure and proper analytics, it is possible for healthcare providers to make key operational decisions. They have begun to adopt a proactive instead of a reactive approach to manage patient flow, alleviate operational bottlenecks, and reduce clinical workload stress. Operational decision-makers are able to make informed decisions.
Appointment Management – Any unexpected gap in the daily calendar can have an adverse financial effect for the healthcare provider while throwing off a clinician's or an office's entire workflow. Healthcare organizations are using analytics to analyze and identify patients' patterns such as the likelihood of skipping an appointment without advance notice. By doing so, it can reduce revenue losses, offer open slots to other patients in need, and minimize the workflow disruption. Duke University conducted a study4 and discovered that by using predictive analytics, there is a higher accuracy than looking at patient patterns to detect a no-show. Providers can leverage this insight to send reminders to patients at risk of failing to show up, offer other services such as transportation to make their appointments, or suggest alternative dates and times that are more suitable.
Financial Risk Management – Similar to other industries, using analytics to analyze risk is highly useful and strategic. Risk management is a burden because it can help and hurt a healthcare organization to determine a patient financial risk of payment and decide on what kind of payment may be appropriate in case a patient does not have coverage. Analytics can help to uncover unpaid bills, identify the cash flow to the hospitals by determining the accounts that demand payment, and also determine which payments are likely to be paid or remain unpaid in the future.
Fraud and Abuse – Fraud and abuse is a big problem and an ongoing issue in healthcare. Leveraging data and analytics can help in detecting and preventing fraud and abuse. There are several types of fraudulent occurrences in healthcare, and they range from honest mistakes such as erroneous billings, to dishonest mistakes such as double charging, wasteful diagnostic tests, false claims leading to improper payments, and so on. Leveraging data and analytics helps in identifying the patterns that lead to potential patterns of preventing fraud and abide in healthcare insurance as well.
Managing Supply Chain – Healthcare providers, hospitals, and clinicians have very tight budgets. The supply chain is one of a provider's largest cost centers and is an area that constantly needs improvement to trim unnecessary spending and improve efficiency. Hospital and healthcare executives are honed into reducing variation and obtaining more actionable insights into ordering patterns and supply utilization. In a survey conducted by Navigant,5 using analytics to monitor the supply chain and make proactive, data-driven decisions about spending could save hospitals almost US$10 million per year. Furthermore, leveraging analytics provides insights to negotiate better pricing, reduce the variation in supplies, and streamline the ordering process.
New Therapies and Precision Medicine – Research and development are constantly evolving and new ideas and innovation are on the cusp of every healthcare practitioner. As precision medicine and genomics gain popularity, researchers and providers are using analytics to augment traditional clinical trials and drug discovery techniques. Analytic and clinical decision support tools play key roles in translating new medicine into precision therapies for the patients. Analytics support the use of modeling and simulation to predict outcomes and design clinical trials, for dose optimization, and to predict product safety and potential adverse effects. In addition, analytics enable researchers to better understand the relationships between genetic variants and how certain therapies can affect the patient.
Healthcare Transformation – There is a shift in healthcare to focus on patient-centered care. Healthcare executives have started to reevaluate how they engage and interact with patients in their care as consumer expectations increasingly demand more personalized and less fragmented healthcare experiences. Consumers have more expectations and know that they have more choices. Not all providers cost the same and even the quality of care is not the same. With analytics, healthcare organizations are able to drive healthcare improvement and transformation, and this will in turn drive impressive levels of change to focus on the patient. Analytics can deliver insight to influence enhancements and changes in the present hospital-centric delivery model from volume and activity to value and outcomes.
Of course, there are many use cases for analytics in healthcare. This chapter only scratches the surface to illustrate the value of collecting the data, making it available so that analytics can be applied and deliver analyses so that clinicians can make insight-driven decisions. In the next few chapters, we will examine closely how people, process, and technology are crucial to further improve the healthcare industry.
NOTES
1 1 DreamIt (24 August 2018). “Just How Big Is the Healthcare Industry? Here's What You Need to Know.” Retrieved from https://www.dreamit.com/journal/2018/4/24/size-healthcare-industry.
2 2 https://www.marketwatch.com/press-release/healthcare-global-market-report-2018-2018-09-17.
3 3 https://www.investopedia.com/articles/investing/042915/5-industries-driving-us-economy.asp.
4 4 https://healthitanalytics.com/news/predictive-analytics-ehr-data-identify-appointment-no-shows.
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