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A Brief Background

  • The percentage of nursing home residents who need help with activities of daily living (ADLs) is increasing (Department of Health and Human Services, Centers for Medicare & Medicaid Services, 2014). In the United States, around 16% of long-term residents experience an increase in their ADL impairments each year.
  • Clinicians, patients, and their caregivers would benefit from information on the next likely ADL events; They would like to know how likely and how long a resident will stay in a particular functional state. While much of the focus in nursing homes is on preservation and restoration of these activities, little information is available to guide patients, providers, families, and policy-makers regarding reasonable expectations regarding the likelihood of and time to functional loss and recovery.
  • Advances in machine learning techniques and availability of large data have enabled us to provide predictions for the likelihood of and time to functional loss and recovery, in addition to survival.
  • These forecasts enable clinicians and patients to better understand what is likely to occur next. The predictions can be used to set priorities for clinical interventions. The targeted and informed rehabilitation interventions may succeed in preventing the next disability. Analysis of large data could also be used for planning purposes as it provides reliable information regarding the time to and severity of the next disability event. Caregivers would like to know how long they need to provide care. Families would like to understand the likely future functioning status of their loved ones.
  • Establishing a patient-centric benchmark is vital to ensuring optimal care for individuals with disabilities in nursing homes. This information can help in planning for coping with disabilities.
  • The purpose of this web-calculator is to provide forecasts regarding the functional status change for residents in nursing homes. Particularly, we predict the likelihood and number of days until the next functional decline and recovery, as well as survival.

Source of Data

We analyzed Activities of Daily Living (ADLs) of 296,051 residents in the US Veterans Affairs nursing homes called Community Living Centers (CLCs) between 1/1/2000 and 9/10/2012. Data were used to classify patients as having feeding (F), bathing (B), grooming (G), dressing (D), bowel continence (L), bladder continence (U), toilet use (T), transfers (S), and walking disabilities (W). In the data, total patients’ visits resulted in 1,820,714 assessments which were used for predictions after excluding the cases with one assessment.

Methods of Analysis

For our predictions, we have used several advanced Machine Learning (ML) algorithms with the above dataset. The technical details of the algorithms will be accessible soon through the scientific articles we are putting forward for publications.

What We Do?

  • This service is free.
  • We collect data on older adults’ age, gender, and current daily living activities. We use these data to predict most likely scenarios (progression, recoveries, and survival) within the next year.
  • The purpose of these predictions is to provide you with scenario-based planning and to make you aware of upcoming likely events.
  • The clinicians and the residents can use this information to prevent unfavorable events (such as progression) or promote the favorable ones (such as recovery).

What We Don’t Do?

  • We do not collect personal information that identifies the residents (e.g., name, phone, email, social security number, etc.).
  • We do not sell data entered into the system.
  • We do not contact you, for any reason, in any manner, unless you contact us.


  • Our forecasts are cross-validated and based on complex data analytics algorithms.
  • Keep in mind that, due to the complex nature of ADLs, the resident’s actual experience may differ from our predictions. No future is for sure, and our predictions may not occur.

Why is this important?

  • Families: duration of care
  • Clinicians: next priority
  • Policy-Makers: value-based reimbursement
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