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The second annual patient leakage & keepage report Health execs say reducing patient leakage could boost revenue by 17%, yet 65% struggle to meet financial goals due to the issue. State of the industry from about reflects this new awareness, but also demonstrates an immediate need for health systems to achieve greater visibility and tighter controls over patient care access and orchestration.

Large healthcare networks were created to broaden patient access to connected providers and facilities Using sentiment analysis, nlp can. Patient leakage is when a patient seeks or obtains healthcare services at a location outside of the hospital network

It may also be referred to as referral leakage or patient referral leakage

There are many reasons why a patient may choose to seek care from another location, such as convenience in location if the patient works in a different area than they live, the need for healthcare. Overview patient leakage occurs when individuals seek care outside their primary healthcare network Healthcare systems must understand the driving forces behind this phenomenon to implement effective retention strategies Patient leakage is one of the most pressing problems facing medical practices today with major cost and care delivery implications

Patient leakage refers to when a patient leaves their healthcare system or provider network to seek care elsewhere This can occur involuntarily due to issues like access limitations or voluntarily based on preferences such as quality and convenience. The solution to patient or referral leakage lies in empowering primary care increasingly, the primary care physician is tasked with the challenge of better utilizing services and specialists currently in their own medical group or network A recent survey of health systems showed that reducing patient leakage is a priority for 94% of respondents but only 10% of them have high confidence in their ability to adequately address the associated drivers

One strategy is to leverage technology like nlp, an arm of ai, to surface additional patient context using the unstructured text in patient records

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