About LTcare.ai
LTcare.ai, created by Medtelligent, Inc., is a data science platform with numerous machine learning models and artificial intelligence technologies specifically designed to give investment groups, corporate teams, and communities the tools they need to elevate their resident’s holistic health and experience, resulting in longer lengths of stay, higher net operating incomes, and high levels of staff and resident satisfaction.
Medtelligent began building LTcare.ai (and its predecessor) and its associated models in 2018.
LTcare.ai has numerous models currently in production, including its Move-Out Predictor, trained on more than 15 years of long-term care industry-specific data.
Who LTcare.ai Is Made For
- All settings across the long-term care continuum (or any congregate care setting with eHR usage) and their stakeholders, including investors, owners, operators, and staff.
- Organizations that want to:
- Evaluate their eHR tool adoption across a community or a portfolio.
- Use their current eHR data to catch changes in acuity and predict patients and/or residents at risk of move-out in the next 90 days to implement interventions to increase the quality of life and ultimately extend the client’s length of stay.
- Use their current eHR data to predict move-outs portfolio-wide to strategically allocate sales and marketing resources and accurately predict future occupancy and revenue numbers.
What Makes It a Game Changer?
Despite its recent introduction to the senior living space, LTcare.ai isn’t a new concept. This innovative prediction tool is 20 years in the making, meticulously designed and developed by LTC professionals who live and breathe long-term care. Years of expertise not only make us incredibly well-versed in this area but have also given the Medtelligent team plenty of time to perfect the system’s capabilities and integrations. What makes this model truly unlike anything else of its kind?
Software Agnostic
For maximum flexibility and ease of use, LTcare.ai models can work on any eHR data set and don’t require any specific hardware to set up, manage, or operate.
Exceptionally Accurate
Highly precise occupancy predictions give visibility into when move-outs will occur, resulting in better alignment between sales and marketing teams.
Fully Transparent
Unlike many other commercial algorithms, LTcare models offer insights into how data sets are analyzed for full transparency into predictive methodology.
Stage 1: Perform Data Hygiene Report
Prior to using LTcare, our team must first assess the adoption level of the eHR tools currently in place with an initial report “grading” each community within a portfolio. This will determine if communities have sufficient data to support LTcare.ai models.
- Gateway level: Get started with LTcare.ai and connect to its functionalities.
- Additional reports: Receive a length of stay report and a portfolio-wide move-out prediction to identify communities in the portfolio with the highest level of move-outs in the next 90 days.
- Benchmarking: Additionally, obtain industry-specific benchmarking data from LTCare.ai for reference. For example, determining how many incidents are “normal” based on other communities across the United States. With more than 15 years of specific industry data, LTCare.ai can help provide such guidance.
Stage 2: Gain Access to Data Models
Once adoption levels and data hygiene are confirmed, the LTCare.ai team begins the official onboarding process for regular reports.
- Weekly: Community and regional teams will receive a list of residents determined to be at risk for move-out in the next 90 days (usually as a result of a change of condition). Intended to augment an existing “high-risk” resident list, this automated list offers real-time visibility into risk across buildings, plus allows care teams to implement additional interventions.
- Monthly: Company leaders and regional staff can receive a report of the portfolio-level move-out predictions.
- Quarterly: On a quarterly basis, the C-level receives updated length-of-stay reports to track progress.
Stage 3: Enable Portfolio Predictions
After laying the groundwork for a successful setup, we conclude the transition with a full-scale integration that permeates throughout the organization, taking all portfolio variables into account for maximum visibility and accuracy.
- Corporate/operator level: LTcare accessibility is extended to the entirety of your portfolio, assessing risk across a wide scope. Operators gain insight into predicted move-outs and forward-looking rent roll for optimized revenue forecasting.
- Executive/capital partner level: Implement strategic resource allocation across the community as a whole to objectively measure risk and gain a competitive edge through smarter insights for better capital decisions.
Data Hygiene Report
This is a report that the LTCare.ai team prepares after getting eHR data from your current system. Information shared includes general census information, all notes/nurses notes/progress notes, incident information, medication information, and other information as needed. This data sharing can be automated for convenience.
Length-of-Stay Report
Move-Out Prediction Model
This is the main applied machine learning model currently used to prepare a list of residents at risk of move-out in the next 90 days. This model, based on the data, provides a weekly report to the corporate team, regional team, and/or community team to flag residents at risk and, if appropriate, kick off workflows and/or interventions related to that resident and to ensure proper care, care level, and follow-up actions.
Portfolio Move-Out Report
This report rolls up the move-out predictions on a community basis into a report for use by the C-suite and/or investor groups. This report is intended to support the strategic allocation of sales and marketing resources as well as other types of planning to optimize net operating income and lengths of stays across a portfolio.