Keith Hovan

Keith Hovan has decades of experience with clinical and executive level healthcare leadership in expansive patient-centered organizations. In the following article, Keith Hovan discusses ways to leverage data in healthcare to foster better decision making among professionals and improve industry quality and safety in patient care.

The healthcare sector is one of the most far-reaching industries in modern times, and its efficacy is heavily reliant on constant analysis and continuous improvement.

The data provided by business intelligence strategies and healthcare analytics can be used to improve decision making in most areas of healthcare delivery. Collected data can be applied to improve patient outcomes and satisfaction, manage costs, increase revenue, improve efficiency, predict disease outbreaks, and provide individualized preventative or curative care in a timely manner.

Below, Keith Hovan investigates how analytics and business intelligence can be used to make better decisions in healthcare management and produce patient-centered benefits.

Data Analysis

Keith Hovan explains that data analysis is one of the most crucial components of informed decision-making in the healthcare industry. The use of veritable data can initiate improvements in business efficiency, the patient’s experience, cost effectiveness and revenue of a facility, and both macro- and micro-disease prediction and prevention.

Improved Patient Outcomes and Satisfaction

Integrating analytic technology into healthcare decision-making produces significant boosts to patient satisfaction and positive outcomes. Analyzed data can be used to identify trends such as infection rates, readmissions, unnecessary care and more. This enables healthcare providers to highlight weaknesses in current medical practices so that they can be corrected and improved upon.

Keith Hovan notes that making use of technology and proven methods in data analysis also allows healthcare professionals to understand where low satisfaction scores may be coming from—insufficient communication with providers or noise in the clinical environment, for example—and take the necessary steps to effectively make positive changes.

Cost Management

Keith Hovan reports that business intelligence analytics have a high potential for helping cost management in healthcare facilities, large or small, such as clinics or hospitals. It can identify the most efficient strategy for budget allocations to different departments, analyze wasted resources from the time of patient admission to their discharge, and provide the opportunity to make error-free decisions with the allocation of financial resources.

Increased Revenue

There are several issues that can arise in healthcare facilities that delay or prevent an organization from receiving insurance reimbursements in a timely manner. To rectify this, the incorporation of business intelligence and data analysis software can have a large positive effect on provider revenue.

Keith Hovan explains that these tools can identify any instances of patient care being provided but not compensated, often as a result of missing or poor documentation, elucidate ways to shorten the period between billing and reimbursement, and improve the efficiency of claims processes.

Enhanced Operational Efficiency

On a broad scale, healthcare analytics and business intelligence can be used to improve the overall productivity of both small and large healthcare facilities. Its ability to illuminate wasted resources, patient bottlenecks, supply chain issues, and staffing problems can allow managers to make more efficient decisions for the betterment of the facility.

Oftentimes, tackling just one or two areas of focus will have noticeable effects on other areas of healthcare management, including patient outcomes, staff satisfaction, and cost-effectiveness.

Keith HovanDisease Outbreak Prediction

Keith Hovan says that since the combination of business intelligence and healthcare analytics incorporates data from every available source, this technology can also predict possible disease outbreaks before they happen to allow medical professionals the opportunity to combat them early and lessen their impact. Vaccination trends, increasing reports of illness or infection, and other factors can also be considered during this process.

Population health management relies heavily on the use of technology and big data analysis – effective policy changes are simply impossible without it.

Preventive Care for the Individual

Businesses intelligence tools and analytics are the cornerstone of effective and individualized preventative healthcare. These systems can assess a patient’s history more extensively than any single clinician ever could, therefore they are more effective at recognizing risk factors for potential diseases and illnesses.

As a result of this technology, Keith Hovan explains that healthcare professionals can recommend treatments to further prevent diseases before they even happen, as a result of being able to identify those at greatest risk for a disease, increasing longevity in comparison to attempting to cure or ameliorate an already active disease.

Not only does this lighten the burden on staff and initiate a string of positive changes within the healthcare system, but it enhances the wellbeing of patients in a meaningful way.

Final Thoughts

As business intelligence strategies and healthcare analytics expand in both scope and accuracy, decision-making processes in healthcare management will continue to develop and evolve.

With the power of big data analysis, healthcare providers can achieve improved patient outcomes, cost management, and disease prevention.

By properly utilizing these tools, providers can optimize their operations, reduce costs, and ultimately provide more personalized care to every patient. With the continued growth of healthcare analytics and business intelligence within the industry, the potential for improvement is limitless.

It is time for healthcare providers to embrace this technology and use it to drive innovation and improve patient care.

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