Predict

The first AI engine built to detect and engage the highest-risk members to reduce MSK and pelvic surgery costs.

The Solution

Predict and prevent unnecessary procedures and related costs.

Predict uses Sword’s AI-powered detection engine to find and engage high-risk members via personalized outreach before they become high-cost claimants.

Predict
Identify the 4% of members who drive 80% of annual MSK and pelvic health costs.
Engage
Engage the highest-cost members with personalization and the support of real humans.
Treat
Treat the members who are most in need with high-quality clinical care.

employer healthcare cost

$71B

annual MSK surgery cost

50%

of surgeries may be unnecessary

Savings

Our advanced algorithms to detect and enroll the highest-cost members will save you millions.

The Holy Grail in healthcare is the ability to drive costs down while, at the same time, materially improving clinical outcomes. With Predict, we are able to deliver exactly that, by meaningfully reducing the need for unnecessary surgeries, while providing members with effective pain relief.

Virgílio “V” Bento

Sword CEO, Chairman and Founder

Personalized approach

Engagement, reimagined

Predictive engagement offers individualized messaging with a human touch. We provide extra care to help the highest-risk members enroll.

Personalization

High-risk members receive messages tailored to their needs instead of one-size-fits-all marketing.

Concierge support

High-cost members can speak with dedicated team members to answer questions and provide technical assistance.

Real-time triggers

Prior authorization and medical claims data feeds ensure that high-need members learn about Sword’s solutions before it’s too late.

STATUS QUO

Why the current solutions don't cut it

Current solutions that use risk models do not result in decreased costs because they lack integration, innovation, advanced engagement, and personalized treatment solutions.

Solutions that only use medical claims fail to differentiate between last year’s high-cost members and future high-cost members. 72% of high-cost members are missed by relying only on prior high-cost claims.

Some solutions engage members only after reviewing medical claims. Due to delays in claims, this risks that a member has already scheduled or completed a surgery.

Many analytics solutions create a list of members at risk but do nothing to engage them.

Solutions