How St. Croix County scaled a careful admissions process with AI-supported referral review

With exacare ai, St. Croix County Health Care Campus moved from a paper-heavy referral process to a faster, AI-supported admissions workflow. The team improved referral-to-admit win rate, reduced average time-to-accept, gained new visibility into referral volume, and gave staff more confidence when reviewing large clinical packets.

Introduction

“It’s like our lifeline.”

St. Croix County Health Care Campus had built a careful, experienced admissions process. The challenge was that the work was becoming harder to sustain as referral volume grew and hospital turnaround expectations increased.

Before exacare ai, referrals arrived by fax from hospitals across Wisconsin and Minnesota, often in packets of more than 100 pages. Krista Nygaard, Campus Director of Nursing, and Lisa Leahy, Director of Social Services, reviewed those packets manually, using handwritten notes, questions, and shorthand to flag clinical, medication, and operational details.

The process reflected the team’s experience and attention to detail. But it also depended on time the team did not always have. Referral review had to happen alongside resident care, meetings, care conferences, wound support, hospital communication, and other day-to-day responsibilities. Important details could be buried deep in a packet, and because referral activity was tracked manually, it was difficult to quantify just how much admissions work the team was managing.

With exacare ai, St. Croix County created a faster, AI-supported admissions workflow. Referrals are uploaded through Quick Upload, summarized, reviewed, flagged, and routed across the team. The result is faster review, clearer visibility into referral performance, and more time for staff to focus on residents, hospital communication, and making the right admissions decision.

Key Results

• 19% higher referral-to-admit win rate
• 92% reduction in average time to accept
• 26% increase in census, moving from an estimated 30–35 residents before exacare ai to 44 at the time of the interview.
• New visibility into referral volume and outcomes: St. Croix County can now quantify long-term care referral activity instead of relying on paper stacks, handwritten notes, and manual tracking.
• More confidence in referral fit: AI-supported summaries and admission flags help the team catch important clinical, medication, and operational details that could previously be missed when reviewing 100+ page referral packets under time pressure.

The Customer

St. Croix County Health Care Campus is part of St. Croix County, a county government entity in western Wisconsin near the Minnesota border.

The campus is located in New Richmond and includes long-term care, transitional care, assisted living, memory care, and a dementia crisis unit. The organization has deep roots in the community, with its original facility dating back to 1897.

Today, the campus includes:

• A 50-bed long-term care and transitional care building
• A 48-bed assisted living and memory care building
• A 10-bed locked dementia crisis unit that opened in early 2025

The team receives referrals from hospitals throughout Wisconsin and Minnesota. Most arrive through traditional fax and include clinical packets, physician orders, encounter documentation, medication information, and other materials needed for an admission decision.

The Stakeholders

Krista Nygaard, Campus Director of Nursing

Krista Nygaard is the Campus Director of Nursing and an internal champion for exacare ai. She is close to day-to-day referral review, admissions decisions, resident care, and staff workflows.

Lisa Leahy, Director of Social Services

Lisa Leahy is a key member of St. Croix County’s admissions workflow. With decades of experience at the campus, Lisa worked closely with Krista to review large faxed referral packets, flag open questions, and coordinate follow-up with hospitals.

The Challenge: Scaling a Careful Admissions Process Built Around Manual Review

Before exacare ai, St. Croix County’s admissions team managed referral review through a highly hands-on process built around clinical experience, collaboration, and deep knowledge of the residents they could safely serve.

Referral packets arrived by fax from hospitals across Wisconsin and Minnesota, often running more than 100 pages. Krista and Lisa had developed a careful review system to work through those packets together. They highlighted important details, added notes and questions, and used a shared shorthand to flag items that needed follow-up, such as POA status, one-on-one needs, medications, and other clinical considerations.

That process helped the team make thoughtful admissions decisions in an environment where referral packets were long, information was scattered, and every decision carried clinical and operational implications.

But as referral volume increased, the process became harder to sustain at the speed hospitals expected. Admissions communication often had to happen within a narrow window, roughly between 8:00 a.m. and 3:30 p.m., before pharmacy coordination became harder and hospital discharge planners wrapped up for the day.

When review took too long, St. Croix County risked missing appropriate referrals simply because another placement decision had already been made.

“We were getting so many referrals that sometimes, by the time we called back, the resident had already been placed somewhere else.”

The pressure also made it harder to review every detail with the level of confidence the team wanted. In a 100-page packet, an important medication, clinical need, or financial consideration could be buried deep in the documentation.

“With 100-page referral packets, it was hard to catch every detail as quickly as we needed to. That could mean missing an appropriate referral or moving forward without the full picture.”

The challenge was not a lack of process or expertise. St. Croix County had both. The challenge was that a careful, paper-based workflow had become difficult to scale as referral volume, packet length, and hospital turnaround expectations increased.

The Solution: A Faster, Shared Admissions Workflow with exacare ai

St. Croix County learned about exacare ai through a peer network for senior care providers. A finance team member heard about the platform in a meeting, brought it to administration, and the team began exploring whether AI could help with referral review.

From the first demo, Krista saw how directly the platform mapped to the work her team was already doing.

“From the first demo, I thought, ‘Yes, this is exactly what we need.”

The team adopted exacare ai using a process called Quick Upload. St. Croix County was not connected through PointClickCare, Epic, or another direct EHR integration, so the team started with a workflow that fit how referrals were already arriving.

Staff scan referral packets, upload them into exacare ai, and enter basic information such as first name, last name, date of birth, sex, and referral source. From there, the team can review AI-supported summaries, recommendation flags, clinical details, rule flags, medication information, PDPM analysis, notes, comments, tags, and activity history.

“They scan it to my email, I drag it into Quick Upload, add the basic referral details, and it’s done.”

The platform gives the team a stronger starting point, but it does not replace clinical judgment. St. Croix County configured admission rules to flag the criteria that matter most for their campus, such as payer requirements, smoking status, medication concerns, or care needs that may vary by setting. When a referral is flagged, the team can quickly see the reason, review the underlying details, and decide whether it is a true barrier or something they can work through.

“Now, when something is flagged, I can quickly see why and decide whether it’s something we can work with.”

The workflow also brought more people into the admissions process. Krista, the nurse manager, administrator, finance team, and admissions coordinator can all use the platform to review, comment, and act on referrals. For clinical leaders, the summaries and flags help identify risks faster. For admissions, the shared workflow helps everyone stay aligned. For finance, summary information helps support prior authorization decisions.

The Implementation: A Practical Rollout Built Around the Existing Workflow

St. Croix County started with Quick Upload rather than a deeper EHR integration, which allowed the team to bring exacare ai into the admissions process without changing how referrals arrived.

The implementation focused on making the workflow easy to adopt: setting up users, configuring admission rules, training the team, and making sure staff knew how to move referral packets from scan to review.

The team went live in September 2025. Because the workflow aligned with what staff were already doing, adoption happened quickly. Referrals could still be scanned and routed the way they were before, but once uploaded into exacare ai, the team had a shared place to review summaries, flags, comments, and referral status.

“It was super easy and user-friendly. You click through, find what you need, and the workflow makes sense.”

One reason the rollout worked was that it did not require every team member to change in the same way at the same time. Staff who were comfortable working digitally could review and act directly in the platform, while team members who preferred paper could still rely on scanned referrals and printed summaries as they adjusted to the new process.

That flexibility helped St. Croix County introduce AI-supported referral review without making the transition feel disruptive for a team already managing a high volume of admissions work.

“It was an easy transition because we saw the benefit immediately. You don’t have to wait to see if it’s working.”

The result was a rollout that felt practical rather than disruptive. St. Croix County could start using exacare ai right away, prove value quickly, and continue refining the workflow as the team became more comfortable with the platform.

The Impact

19% higher referral-to-admit win rate

Before exacare ai, the St. Croix County had a 70% referral-to-admit win rate. By April 2026, that monthly win rate had increased to 83%. That represents a 19% relative increase.

For St. Croix County, the improvement reflects more than faster referral review. It shows the team was able to identify appropriate residents, respond with more confidence, and move more accepted referrals through to admission.

92% reduction in average time to accept

St. Croix County also reduced average time to accept inside exacare ai. By May 2026, average time to accept had decreased 92%.

This metric reflects the time between a referral entering exacare ai and being marked accepted in the platform. It is separate from Krista’s experience of day-to-day referral decision-making, but both point to the same operational shift: the team could move from referral intake to action much faster.

26% increase in census

One of the most visible outcomes for St. Croix County was census. Krista estimated that long-term care census had previously averaged around 30 to 35. By April 2026, census was 44.

Using the most conservative end of that estimated prior range, that represents at least a 26% increase.

Krista connected that improvement to faster response times and fewer missed opportunities. Before exacare ai, the team sometimes lost appropriate referrals because another placement decision had already been made by the time they could complete review and follow up.

With exacare ai, St. Croix County could review referrals sooner, communicate with hospitals faster, and keep more appropriate residents moving through the admissions process.

New visibility into referral volume and outcomes

Before exacare ai, St. Croix County had a clear sense that referral volume was high, but it was difficult to quantify. Referral activity lived across fax packets, handwritten notes, staff memory, and manual tracking.

“We knew we were busy with referrals, but we couldn’t put a number on it.”

With exacare ai reporting, that changed.

That visibility gave the team a clearer view of referral activity, acceptance, movement, and workload. It also helped support the need for a dedicated admissions coordinator, who started March 2026.

The reporting gave the team data they simply did not have before.

More confidence in referral fit

exacare ai also helped the team review referral fit with more confidence. Instead of searching through a 100-page packet from scratch, staff could start with AI-supported summaries, recommendation flags, rule flags, medication information, and other key details surfaced for review.

That mattered because St. Croix County still relies on clinical judgment. The team reviews every referral carefully, but now they can see why something was flagged and decide whether it is a true barrier or something they can work through.

“We still look at every referral. But now, when something is flagged, I can quickly see why and decide whether it’s something we can work with.”

The impact showed up beyond the admissions team. Contracted therapy partners began noticing a difference in the residents coming in.

“Our therapists started asking if we were doing something different, because we were getting really strong rehab candidates.”

For Krista, that feedback reinforced the value of a more informed review process. The goal was not simply to move faster. It was to move faster while making better-fit admissions decisions.

Staff got time back for higher-value work

The impact was also personal for the team. Before exacare ai, referral review had to be worked in around resident care, meetings, care conferences, wound support, hospital communication, and other clinical responsibilities.

After implementation, Krista described the time returned to her and Lisa as one of the biggest changes.

“The amount of time exacare ai has given back to us is amazing.”

Admissions review is not just an administrative task. That time back impacts residents, families, hospital partners, clinical teams, finance, and the broader campus.

“We’re caring for people. We should not have to rush through that.”

What’s Next

St. Croix County sees exacare ai as part of a broader shift toward faster, more connected admissions workflows.

The team is interested in new capabilities that could extend the same efficiency gains beyond referral review, including eSign for admissions paperwork. For Krista, digitizing more of the admissions process is a natural next step, especially as the new admissions coordinator takes on more of the day-to-day workflow.

St. Croix County is also interested in future home health and hospice-related opportunities. Krista mentioned Lakeview Hospice and Home Health, and Adoray, as important partners, and sees potential for more connected workflows across the care partners involved in a resident’s transition.

For Krista, the reason to recommend exacare ai is simple: it gives teams time back, supports better decisions, and helps facilities stay focused on residents.

“Don’t wait to get exacare ai. You will not regret it.”

For St. Croix County, the future is not just faster referral review. It is an admissions process that gives staff more time to think, more confidence in each decision, and more room to focus on the people behind every packet. With the right information in front of them sooner, the team can help hospitals move faster, support families with more clarity, and get residents to the care setting that is right for them.