Healthcare Complexity Simplified with Cutting-edge AI
In the healthcare ecosystem, the hype around AI has created chaos among providers and payers in identifying an expert that can help them leverage these technologies to enable better patient care. “At DocSynk, we end this dilemma through our smart and cost-effective AI solutions for healthcare,” says the company’s Founder and CEO, Vaidyanatha Siva, an industry veteran with over 20 years of experience serving as the CTO of brands like Parkland Center for Clinical Innovation and Infosys. DocSynk’s pragmatic “AI Inside” solution is a one-stop-shop that provides value for the patients, physicians, and payers and solves some of the most critical issues revolving around population health management (PHM), revenue cycle management (RCM), and patient engagement.
Unique facets of the DocSynk AI include a population health model, which identifies patients who are at risk of early-onset diabetes, COPD (Chronic obstructive pulmonary disease), and CHF (Congestive heart failure ) among several others. The model also enables an organization to find patients at risk, even if they are infrequent visitors. This proves the versatility of the DocSynk AI engine.
Siva highlights a population health management company with a market value of $250 million, whose only AI partner has been DocSynk.
Our cutting-edge solution is incredibly smart in enabling a human-like conversation with the patient
The organization, with a client base of over 130 healthcare organizations, covers 18 million lives and over two billion healthcare data points. DocSynk has used its unique platform to assist the company in identifying the patients at risk of a chronic condition and predicting those that are prone to any adverse events.
Through its RCM engine, DocSynk profitably impacts the critical stages of a revenue cycle, including contract management and denials, overtaking the static rules-based engines and other manual interactions. The result is a better focus on financial outcomes and overall efficiency. Using pattern matching, computer vision, OCR, and machine learning, DocSynk has enabled another top-4 RCM vendor to achieve increased contract analysis coverage to 100 percent and automated contract ingestion with 96 percent accuracy. This combination of 100 percent coverage (up from the industry average of 75 percent) and high accuracy enables providers to enhance their topline by as much as 15%, which translates to millions of dollars for hospitals and large physician groups.
Every healthcare provider’s success depends on their ability to meaningfully engage patients.
From transactional support like scheduling or rescheduling appointments to supporting a patient in complying with treatment plans, DocSynk provides a unique solution. “Our conversational AI solution is incredibly smart in enabling a human-like conversation with the patient, making his life easier, improving clinical outcomes, and reducing treatment costs,” adds Siva. This solution is being rolled out in production in a leading cancer care center, hospital system and medical school in the state of Tennessee, where it will support a covered population of over 10 Million.
Having walked a three-year journey in delivering a quick-to-market, powerful, and easily extensible solution, DocSynk has proven its prowess in AI among Providers, Payers, and more. With its seasoned team comprised of engineers from prominent universities and an advisory board of expert leaders from healthcare, the company envisions to deliver groundbreaking solutions to the healthcare landscape, leveraging its proficiency in AI and machine learning.
Currently focused on the U.S. market through a channel market approach, working with blue-chip partners to be their “AI Inside” platform, DocSynk is in talks with the top brands. Scaling its technological capability, the company is currently devoting it efforts to rolling out its bot in the clinical space. Proudly toting the principle of “under promise, over deliver”, DocSynk stands its ground in helping organizations steadily move from a traditional healthcare approach toward the world of AI and machine learning.
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