A guide to healthcare digital transformation and the digital-first model

A guide to healthcare digital transformation and the digital-first model

Health

Besides being all the rage in the industry, digital transformation is a key component to the strategy of forward-leaning hospitals and health systems today. It aligns healthcare organizations with its customers – highly digital patients.

This is one of the most important topics healthcare CIOs and other C-suite and health IT leaders can be talking about today. Which is why we sat down for a chat with Dr. Gauri Puri, chief business officer, healthcare and life sciences business unit, at WNS, a global business process management company that works with businesses across industries, including healthcare.

Here we discuss what successful digital transformation of processes like administration, revenue cycle and clinical management looks like; a digital-first model for clinical and revenue cycle management, and how providers can approach adopting the model; and how emerging technologies can help providers prevent issues like revenue leakage and payment delays.

Q. What does successful digital transformation of processes like administration, revenue cycle and clinical management look like to you?

A. This encompasses several key elements. First, it involves optimizing workflows to eliminate manual tasks and streamline operations.

In our experience, 60-70% of scheduling and appointments are manual, and patients do not have simplified and intuitive apps or portals for curating their own care. Similarly, seamless data exchange across hospitals, health insurance and regulatory systems is crucial for efficient functioning. Automation driven by artificial intelligence and analytics enhances productivity and accuracy across various tasks.

In RCM, for instance, manual processes from coding to claims submission are replaced with technologies like robotic process automation, optical character recognition and generative AI.

These technologies automate tasks such as patient eligibility verification, charge capture and denial management, ensuring high data accuracy and faster turnaround times. AI-powered coding and predictive analytics further optimize revenue generation and cash flow.

Similarly, hyper-automation streamlines administration processes such as appointment scheduling and patient registration, integrating them into a unified platform. GenAI-based chatbots empower patients to self-serve, while automation bots handle time-consuming tasks like data entry, allowing administrative staff to focus on value-added activities.

In clinical management, automation, AI, machine learning and genAI technologies revolutionize workflows, enabling real-time access to comprehensive patient data and clinical guidelines. Automated clinical workflows, transcription and clinical note generation enhance efficiency and accuracy, while AI-enabled decision support systems optimize patient care and staff productivity.

This solves a critical problem for nurses and doctors, who currently spend most of their time in administrative and clinical information gathering and still lack access to the most appropriate clinical guidelines or an updated 360-degree view of patient information.

Data integration and interoperability are critical components of successful digital transformation in healthcare. Seamless connectivity between systems enables better decision making and coordination, improving patient outcomes and operational efficiency.

However, holistic transformation goes beyond technology implementation. It involves developing a comprehensive strategy, establishing robust technology and data foundations, building scalable operating models, and driving change management for digital adoption. By embracing these elements, healthcare organizations can navigate the complexities of digital transformation and deliver enhanced value to patients and stakeholders alike.

Q. What is a digital-first model for clinical and revenue cycle management, and how can providers approach adopting the model?

A. A digital-first model for clinical and RCM prioritizes the use of digital technologies, data-driven approaches and a digital mindset to streamline operations, improve efficiency and enhance patient care outcomes.

It aligns with the Quadruple Aim of healthcare: enhancing patient experience; improving population health; reducing costs; and augmenting the work life of healthcare providers.

From the patient’s perspective, a digital-first approach means enabling: teleconsultations or visits through their preferred channel; personalized self-service appointment booking; and transparent access to their healthcare data.

This is facilitated by intuitive portals, AI-driven insights and virtual care options.

For RCM teams, a digital-first model comprises implementing touchless prior authorizations and claims, automating data capture and coding processes, and leveraging advanced analytics for data-driven decision making. This allows teams to prevent denials, analyze payments and conduct efficient accounts receivable follow-ups.

In clinical management, a digital-first approach enables personalized care by leveraging comprehensive patient data mapped to clinical guidelines. GenAI is used to automate the capture of relevant patient-provider interactions, enhancing clinical decision making and patient outcomes.

To adopt a digital-first model, provider organizations should prioritize patient needs and reimagine processes, systems and engagements accordingly. This involves implementing digital workflows, digitizing processes and enhancing customer-centric experiences.

Integrated digital platforms encompassing EHRs, practice management systems and RCM technologies are essential, facilitating seamless communication and data sharing between clinical and administrative departments.

Providers should opt for digital portals for patient onboarding, appointment scheduling, medical record access and handling of patient queries, promoting better engagement and empowerment. For clinical processes, automated workflows and AI-enabled decision support systems improve turnaround times, enhance clinical staff experience and boost productivity.

Adopting the right digital-first model begins with defining a digital strategy and assessing current processes to identify transformational opportunities. Providers should clearly define goals, assess workflows and technology infrastructure, and address pain points in clinical and RCM operations.

Building the right talent pool, establishing cross-functional teams and fostering a culture of agility and innovation are crucial for success. Strong governance and change management practices ensure effective program management and adoption, while robust data foundations drive insights and end-to-end transformation.

Overall, a digital-first approach empowers providers to deliver high-quality care, improve operational efficiency, and drive positive outcomes for patients and stakeholders.

Q. How can emerging technologies help providers prevent issues like revenue leakage and payment delays, and what are these emerging technologies?

A. Emerging technologies play a crucial role in helping healthcare providers prevent issues like revenue leakage and payment delays, ultimately ensuring a healthy top line and enhanced cash flows. Advanced analytics serves as the cornerstone for detecting the root causes of revenue leakage, AR aging and denial reasons.

Providers can leverage a combination of technologies, including data analytics, AI and ML algorithmsbusiness intelligence tools, and predictive analytics, to optimize their RCM processes.

One key area where emerging technologies are making a significant impact is patient collections and AR management. By harnessing data analytics and predictive modeling techniques, providers can assess patients’ propensity to pay and prioritize collection efforts accordingly.

Collections analytics can predict the likelihood of timely payments based on historical data and demographics, enabling collection agents to tailor their strategies and communication channels for maximum effectiveness. This targeted approach has led to notable improvements, with a 20-30% increase in collection rate for our clients.

Additionally, collections analytics assists providers in devising effective debt recovery strategies for patients who fail to pay. By identifying the most appropriate communication channels and offering personalized payment plans, providers can minimize bad debt rates, leading to a more robust financial performance. We have witnessed this strategy deliver a 10% reduction in bad debt rate for our clients.

On the payer side, AI and ML algorithms analyze vast amounts of claims data to identify patterns and predict high-risk claims likely to be denied. This proactive approach enables providers to intervene preemptively, addressing potential payment delays and reducing the overall denial rate. Furthermore, leveraging genAI-powered coding tools enhances coding accuracy and minimizes coding-related denials, further optimizing RCM processes.

Overall, collections analytics and shift-left analytics initiatives aim to improve financial performance, reduce bad debt and enhance the patient experience by streamlining revenue cycle and payment collection processes. By leveraging data-driven insights and emerging technologies, healthcare providers can make their RCM processes more efficient and effective, benefiting providers and patients alike.

Follow Bill’s HIT coverage on LinkedIn: Bill Siwicki
Email him:bsiwicki@himss.org
Healthcare IT News is a HIMSS Media publication.

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