The advent of big data analytics has revolutionized the way we handle, examine, and harness data across various industries. One of the sectors witnessing profound transformations through data analytics is healthcare.
When deployed effectively, data analytics in healthcare has the potential to lower treatment expenses, provide in-depth insights into patients and the conditions impacting or with the potential to impact them and enhance overall quality of life.
In the realm of healthcare data analytics, the fusion of historical and real-time data not only anticipates emerging trends but also unveils actionable insights, catalyzes breakthroughs in medical advancements, and propels sustained long-term growth.
Beyond the factors mentioned earlier, healthcare entities are increasingly embracing big data analytics to mitigate excessive expenditures associated with inefficient stock management, patient care, and staff allocation. This article predominantly centers on how data analytics is aiding medical institutions and professionals in enhancing patient care while simultaneously curbing costs.
Prior to proceeding, let’s delve into the primary categories of healthcare analytics and explore their potential to instigate transformative change within the healthcare sector.
What are the types of data analytics in healthcare?
Four distinct types of healthcare analytics can be employed, tailored to the objectives and requirements of healthcare professionals and institutions. Each type plays a critical role in optimizing healthcare data based on the specific context.
Descriptive Analytics:
This type offers a retrospective perspective on data, enabling healthcare providers and management to assess the efficiency of current practices and make recommendations as needed.
Predictive Analytics:
Utilizing modeling and forecasting techniques, predictive analytics aims to ascertain future occurrences. This enables healthcare providers to compute risk scores for individual patients, pinpointing those who may necessitate additional attention. It’s important to note that while predictive analytics can identify potential risks, it does not inherently suggest measures for preventing adverse events, such as hospitalization.
Diagnostic Analytics:
Serving to comprehend the reasons behind specific events, diagnostic analytics facilitates the identification of actions to address issues effectively. This type of analytics is valuable for unraveling the events and factors that contributed to a particular outcome. Similar to descriptive analytics, diagnostic analytics involves an examination of historical data.
Prescriptive Analytics:
Enabling an understanding of the necessary actions to alter predictions, this form of analytics employs advanced algorithms to assess the potential effects of specific actions. By leveraging historical data, it offers solutions tailored to achieve specific outcomes. Regarded as the most valuable and potent type of analytics, prescriptive analytics furnishes guiding recommendations for healthcare professionals.
Key Strategies for Lowering Healthcare Expenditures through Data Analytics
Implementing data analytics and visualization effectively can enhance patient access to services, leading to reduced costs, increased revenue, and heightened patient satisfaction. In the upcoming section, we will explore various approaches to cost reduction in the healthcare sector through the utilization of data analytics tools and solutions.
Electronic Health Records (EHRs):
Digitizing medical records is a significant advantage of employing data analytics in healthcare, leading to considerable cost savings. EHRs accumulate vast amounts of data encompassing a multitude of clinical information. This includes real-time updates of administrative and diagnostic patient information during each encounter. Specifically, EHRs furnish details on procedures, demographics, length of stay, and associated fees.
Moreover, they improve efficiency by simplifying routine tasks, minimizing errors, and expediting data access and entry, thereby leading to substantial cost reductions in the healthcare sector.
Predicts Operating Room Requirements:
The construction, operation, and staffing of operating rooms entail significant expenses for hospitals. Therefore, every healthcare facility must maximize the utilization of operating rooms without jeopardizing patient health. To attain this objective, numerous healthcare providers and administrators are employing data analytics to comprehend the intricate connections among various variables affecting operating room scheduling. These variables encompass surgeon availability, operating hours, and the functionality and availability of equipment.
Crafting a surgical schedule is a complex task due to numerous considerations. Fortunately, data analytics offers a solution to streamline the management of operating rooms. Hospitals are inundated with data related to staff, patients, and facilities. The optimal approach involves extracting relevant data, analyzing patterns of operating room utilization, and employing predictive modeling to align the most suitable human and support resources with the appropriate operating rooms.
As an example, healthcare institutions such as UCHealth in Colorado have transitioned to mobile applications utilizing cloud computing and predictive analytics to enhance the efficiency of operating room utilization and eradicate scheduling conflicts. Following this transition, UCHealth experienced a 4% surge, equivalent to $15 million, in annual revenue from surgical procedures.
Optimizing Staffing:
In numerous healthcare facilities, impromptu scheduling often occurs without considering various factors, potentially resulting in staff shortages that impact patient care. Precise staffing is crucial, given that half of a hospital’s budget is allocated to labor costs. Data analytics is now aiding administrators and managers in proactively addressing staffing challenges, forecasting at least 30 days in advance. Analytics for enterprise ventures employs intelligence to predict and analyze historical staffing figures, local weather patterns, seasonal infections, holidays, and other relevant factors specific to each organization. This approach leads to reduced labor costs and efficient, cost-effective shift management.
Prevents Hospital Readmissions within 30 Days:
The prevalence of unnecessary readmissions poses a significant challenge in the US healthcare system, imposing an avoidable financial burden on hospitals with limited resources. The prospect of cost reduction lies in minimizing readmissions. Data analytics tools prove instrumental in identifying patients exhibiting specific symptoms and diseases that contribute to their readmission. This empowers healthcare providers to implement additional measures aimed at preventing patients from returning within the critical 30-day window.
Analytics tools can be employed to create a personalized heat map for each patient who has remained out of the hospital for the preceding 30 days. Healthcare professionals can analyze user-friendly visual representations of the data, pinpointing specific periods within this 30-day window where the patient is particularly vulnerable. This valuable information aids in strategizing and implementing additional actions to ensure the patient’s well-being.
As an illustration, data scientists at NYU Langone Medical Center have devised a predictive analytics algorithm incorporating diverse clinical factors. This algorithm assists in identifying patients with a likelihood of spending fewer than two nights in the hospital. The tool further aids physicians in determining the appropriate timing to place a patient under observation.
Prevents Missed Appointments:
When patients fail to attend scheduled appointments, the resultant unexpected openings in a practitioner’s daily schedule can lead to financial implications and disrupt workflow. Employing data analytics to identify patients prone to missing appointments without prior notice can substantially reduce revenue loss. This approach enables medical professionals to allocate open slots to other patients, thereby improving customer experience. A study conducted at Duke University revealed that data analytics could accurately capture 4,800 instances of patient no-shows per year.
Utilizing data analytics tools can also enable the anticipation of when patients might present themselves at facilities like urgent care centers or emergency departments, where schedules are not predetermined. This, in effect, could enhance staffing adequacy and reduce waiting times.
Improves Management of Supply Chain Costs:
Like any other business, hospitals depend on an efficient supply chain for seamless operations. Disruptions in the supply chain can adversely impact patient care and treatment, resulting in revenue loss for hospitals.
Prevents Fraud and Strengthens Security:
Hospitals face ongoing challenges with data breaches and fraudulent activities. In June 2020, the Department of Health and Human Services in the US observed a surge in data breaches as cybercriminals capitalized on the distractions caused by the COVID-19 pandemic. Such cybercrimes impose significant costs on hospitals, leading to an average revenue loss of nearly $3 million. To counteract data breaches, hospitals are leveraging data analytics to detect alterations in network traffic patterns and identify other suspicious online behaviors.
Additionally, data analytics enables organizations to monitor inaccurate or fraudulent payments. This streamlines the claims process, ensuring faster reimbursements, and effectively tracks and addresses incorrect payments.
Reduces Medical Errors:
Annually, approximately 400,000 patients are impacted by medical errors stemming from surgical, diagnostic, medication, and other lapses, resulting in hospitals losing billions of dollars in revenue. Many errors occur due to staff negligence or inadequate information. The application of big data can play a pivotal role in reducing these medical errors by scrutinizing the patient’s medical records, analyzing all prescribed treatments, and flagging any anomalies.
Having explored how data analytics contributes to cost reduction and the advantages it brings to healthcare, let’s now delve into the prospects of data analytics in the healthcare industry.
The future of data analytics in healthcare
Data analytics is currently playing a pivotal role in reshaping the healthcare sector, contributing to the delivery of high-quality care, and reducing overall costs for both patients and medical institutions. Its influence is poised to grow significantly in the years to come. Big data analytics in healthcare will see an escalating use in predicting the likelihood of future scenarios, facilitating well-informed and improved decision-making.
Anticipated technological and organizational progressions will result in an abundance of available data, suitable for the development and deployment of advanced data analytics tools and systems. The future is expected to witness an increase in processing power, accompanied by the availability of more off-the-shelf analytical tools, making these advancements more accessible and commercially viable.
How Digiatto It Services can help with data analytics
As demonstrated, the influence of data analytics in healthcare is immense. It serves as a crucial factor in diminishing costs for hospitals and elevating the overall quality of patient care. Consequently, there has been a substantial increase in the embrace of big data analytics within the healthcare industry.
If you’re seeking assistance in integrating big data technology into your business, reach out today to our data analysis team. We’ll collaborate with you to pinpoint areas where big data can offer the most benefits and facilitate its seamless incorporation.
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