Driving Patient-Centricity: The Role of Data Processing in Healthcare Industry

The healthcare industry is undergoing a massive transformation. Rather than being passive recipients of medical services and care, patients have become more empowered and are asking for more patient-centric models. Competition is wild and the topline for healthcare organizations sits delicately on delivering unparalleled patient experiences and optimizing operational efficiency.

This raises the question: How can healthcare providers achieve and maintain this equilibrium?

Surprisingly, the answer rests with the often underused, yet the most powerful asset: data.

The concept of healthcare data stands right at the cross-junction of healthcare and technology, with the promise to transform the management, delivery, and accessibility of medical services and patient care. In practice, healthcare providers are utilizing medical, hospital, and clinical data through data processing services to provide personalized healthcare, improve clinical outcomes, and streamline non-clinical processes. However, the power of healthcare data extends beyond this but is easier said than done!

Understanding the Key Growth Drivers of Modern-Day Healthcare Industry

To understand the transformative powers of data in fostering exponential yet sustainable growth for healthcare providers, it is crucial to have a clear understanding of the key pillars of this growth. Historically, the healthcare industry was dominated by the daily number of procedures performed, bed occupancy rates, outpatient department registrations, etc. However, the equation has changed as these pillars are metamorphosed into a more patient-centric model.

• I. Patient Experience: For modern patients, healthcare is like any other service. They seek transparency, convenience, and hyper-personalized care. Lack of real-time communication, longer wait times, and impersonal interactions lead to dissatisfaction and an increased churn rate.
• II. Operational Efficiency: Streamlined operations and better collaboration between clinical and non-clinical departments not only reduce overheads but also enhance patient satisfaction. Imagine the chaos of siloed, unstructured patient data or errors due to manual data entry. By removing these bottlenecks, healthcare organizations can drastically improve operational efficiency.
• III. Collaborative Care: Siloed patient data within departments or clinical data stored in disparate repositories can impact patient outcomes and clinical research. Breaking down these data silos to ensure efficient collaboration between specialists, departments, and healthcare organizations can result in holistic, collaborative, and coordinated care.

Adopting Data-Driven Strategies to Achieve Healthcare Growth Drivers

The initial step of leveraging data to achieve the three growth drivers is to break data silos, transform raw, unstructured data into structured and formatted databases, and organize it at one central data repository for further analysis. These tasks require the expertise of data processing companies, with the domain knowledge of the healthcare industry to ensure regulatory compliance. Once performed successfully, the transformed data and generated insights can be used as fuel to drive the following strategies:

• 1. 360° Patient View: Imagine having an eagle’s eye view of your patient’s journey, from appointment scheduling and test results to post-treatment rehabilitation and medical history. This holistic view can be gained by collecting data from disparate sources such as electronic health records (EHRs), patient’s medical files, and patient portals, integrating them, and transforming them into a structured format. Through this data processing, healthcare providers can understand a patient’s diagnosis, prognosis, health history, and engagement patterns in a better way.
• 2. Operational Analytics: Healthcare is a complex model with various branches, creating volumes of data. It is a storehouse of valuable insights into improving operational efficiency. However, generating actionable insights from raw, siloed data requires the expertise of a data processing consultant. These insights are a steppingstone to detecting bottlenecks in areas like resource allocation, care delivery, and patient management while streamlining end-to-end operations for reduced patient wait time and optimized resource utilization.
• 3. Hyper-Personalized Patient Engagement: Avoid treating all patients the same way. Each patient is unique in their journey, which requires unique communication. Harness patient data like diagnoses and treatment plans from their medical records and EHRs to customize outreach and interactions. This could involve offering custom wellness programs, educational materials to support them during their recovery phase, or targeted reminders for medications, tests, or appointments. Such personalized engagement improves patient relationships and adherence to the treatment plan.
• 4. Predictive Health Modeling: By integrating, processing, and analyzing various data points for a specific populace, future health trends, the populace’s health needs, and even potential outbreaks can be predicted. These predictions facilitate healthcare providers, government agencies, and NGOs to follow a proactive approach toward resource planning and care delivery. Imagine being able to pinpoint individuals with a higher risk of chronic disease and intervene early with physician-approved preventive measures.

Turning Data into Action: Hypothetical Use Case

Let’s assume a real-world instance. Think of a mid-sized hospital with an outstanding reputation for advanced medical treatment and care. Despite its capabilities and brand name, the hospital was experiencing patient dissatisfaction and a decline in loyalty. The reason? Longer waiting times and a general notion of lack of personalized interaction.

To address these challenges, the hospital partnered with a leading data processing firm and kickstarted its data transformation journey. They deployed a comprehensive strategy to integrate and process all patient data and used it to personalize their patient’s journey and communication and predictive modeling. The result?

• I. Proactive Care: Based on the collected patient data, predictive modeling helped physicians identify high-risk patients for certain disorders. Such insights enabled early preventive measures, resulting in improved patient outcomes and reduced treatment costs.
• II. Reduced Wait Times: In-depth analysis of appointment scheduling, patient flow, and amount of time spent at a specific department helped hospital administration identify bottlenecks and optimize patient scheduling and flow processes. This resulted in a significant reduction in waiting time, one of the key factors of patient satisfaction.
• III. Improved Patient Communication: Personalized and highly targeted communication strategies were designed and implemented based on historical patient interactions and their preferences. Two-way communication channels, easy-to-understand educational materials, and timely reminders fostered lasting relationships with patients.

The overall impact was significant. There was a visible improvement in patient satisfaction, resulting in an enhanced patient retention rate and new registrations seeking a more empathetic and patient-centric experience, powered by word of mouth. Though a hypothetical case, it undeniably showcases the transformative power of connected, well-structured, and thoroughly processed data.

Learning the First Step Toward Data Transformation

Adopting a data-driven strategy may seem daunting, but going for an all-or-nothing approach isn’t necessary. Following are some initial measures healthcare organizations can start with:

• a. Identification of Key Performance Indicators: Establish the most important metrics for your organization, such as wait times, patient satisfaction rate, and readmission rate.
• b. Partnering with a Reliable Data Management and Support Company: Collaborate with a reliable data management services provider to deploy a data processing strategy and a centralized and secure data storage system to connect disparate data sources.
• c. Make Your Workforce Data Literate: Train your workforce to understand the importance of even the smallest data collected and empower them to interpret the collected data. You can ask your data management partner to conduct training workshops and programs to achieve this goal.
• d. Begin with Small, Scale Later: Start with a pilot project, focusing on a small area such as reducing wait time. Once you achieve success in this goal, other initiatives can be scaled up.

Conclusive Thoughts

For healthcare providers striving for expansion, sustainable growth, and satisfied patients, data is no longer just an idea; it is a strategic asset waiting to be leveraged. By adopting modern data processing techniques and analytical tools and deploying data-driven strategies, healthcare organizations can gain an in-depth understanding of their patients, streamline operations, and deliver holistic patient care. As the healthcare landscape continues to transform, those who realize the potential of data will lead the way in delivering efficient, high-quality, and patient-centered experiences.