In senior living facilities, some of the most common health related problems stem from the GI tract and urinarysystem including Clostridium difficile Infections (CDI), Urinary Tract Infections (UTI), Constipation and ColonCancer. The best way to lower costs and treat these conditions effectively is early diagnosis and treatment.Current clinical management for these conditions, as well as many others, include monitoring of specific excretacharacteristics including urine color, urination frequency, urine duration, stool color, stool frequency and stoolconsistency. These logs are the best tool doctors currently have to screen for conditions such as UTIs, infectiousdiarrhea, dehydration, chronic kidney disease, GI bleeding, GU surgery, inflammatory bowel disease, andconstipation, that can lead to hospitalizations and readmissions. However, these logs are on average 61%inaccurate at reporting adverse episodes such as diarrhea. Toi Labs has developed the patented TrueLootechnology to take pictures of excreta and, using machine learning algorithms, classify the toileting event usingDigital Biomarkers (DBMs). The ability to create an excreta log to accurately deliver detailed information todoctors and healthcare providers can revolutionize healthcare by notifying when further screening (urine or fecal)is necessary. This novel, low-cost approach of machine learning and image identification technology thatrequires no change in behavior of the user will enable currently undetectable links between medical records andspecific excreta patterns. In the future, the machine learning algorithm may be able to determine links betweenthese excreta logs and the onset of specific diseases. In this study we will be collecting manual excreta recordsand automated TrueLoo digital excreta records in memory care and assisted living facilities, and compare themagainst each other and against patients' deidentified medical records. We will determine correlative data betweenexcreta logs and adverse events to establish conditional threshold for each type of adverse event and, using ML,try and establish individual conditional thresholds for reporting to caregiving staff. We will compare the manualand digital logs to assess the difference between speed of adverse episode identification when using TrueLooas compared to manual logs. NARRATIVE (max 3 sentences)
Assisted living facilities and memory care facilities have a high rate of health problems stemming from GI tract
and urinary system issues including diarrhea, constipation, urinary tract infections, and colon cancer to name a
few. The ability to create a digital bowel movement and urine log to accurately inform doctors and healthcare
providers detailed and correct information can revolutionize healthcare by providing information on when further
screening (urine or fecal) is necessary. Toi Labs has created TrueLoo, a specialized toilet seat, that captures
images to log the bathroom session and is building a computer algorithm to determine links between toilet logs
and the onset of diseases. Affect ; Elderly ; advanced age ; elders ; geriatric ; late life ; later life ; older adult ; older person ; senior citizen ; Algorithms ; Antibiotics ; Antibiotic Agents ; Antibiotic Drugs ; Miscellaneous Antibiotic ; Behavior ; Blood ; Blood Reticuloendothelial System ; Cause of Death ; Clinical Research ; Clinical Study ; Color ; Constipation ; Coprophagia ; Defecation ; bowel movement ; Dehydration ; body water dehydration ; Diarrhea ; Disease ; Disorder ; Feces ; stool ; Fecal Impaction ; Female ; Future ; Gastrointestinal Hemorrhage ; GI bleeding ; GI hemorrhage ; gastrointestinal bleeding ; Gastrointestinal tract structure ; Alimentary Canal ; Digestive Tract ; GI Tract ; Gastrointestinal Tract ; alimentary tract ; digestive canal ; Goals ; Health ; Health care facility ; Health Facilities ; Healthcare Facility ; care facilities ; Health Personnel ; Health Care Providers ; Healthcare Providers ; Healthcare worker ; health care personnel ; health care worker ; health provider ; health workforce ; healthcare personnel ; medical personnel ; treatment provider ; Hospitalization ; Hospital Admission ; Independent Living ; Infection ; Inflammatory Bowel Diseases ; Inflammatory Bowel Disorder ; Chronic Kidney Failure ; Chronic Renal Disease ; Chronic Renal Failure ; chronic kidney disease ; Lead ; Pb element ; heavy metal Pb ; heavy metal lead ; Long-Term Care ; extended care ; longterm care ; male ; Manuals ; Medical Records ; Methods ; Morbidity - disease rate ; Morbidity ; Names ; Legal patent ; Patents ; Patients ; Pilot Projects ; pilot study ; Primary Health Care ; Primary Care ; Primary Healthcare ; Records ; Reference Standards ; Reference Values ; Reference Ranges ; Technology ; Testing ; Time ; Urinary tract infection ; Urinary tract infectious disease ; urinary infection ; Urination ; micturition ; Urine ; Urine Urinary System ; Clostridium difficile ; C diff ; C difficile ; C. diff ; C. difficile ; Clostridioides difficile ; Antibiotic Resistance ; Resistance to antibiotics ; Resistant to antibiotics ; antibiotic drug resistance ; antibiotic resistant ; Healthcare ; health care ; Data Set ; Dataset ; base ; Left ; Clinical ; Phase ; Medical ; Link ; residence ; residential building ; residential site ; Individual ; Onset of illness ; disease onset ; disorder onset ; fluid ; liquid ; Liquid substance ; tool ; instrument ; machine learned ; Machine Learning ; Frequencies ; Event ; Source ; Pattern ; System ; Operative Procedures ; Surgical ; Surgical Interventions ; Surgical Procedure ; surgery ; Operative Surgical Procedures ; early detection ; Early Diagnosis ; hospital re-admission ; re-admission ; re-hospitalization ; readmission ; rehospitalization ; hospital readmission ; Speed ; novel ; Self-Report ; Patient Self-Report ; Colon Cancer ; Colonic Carcinoma ; cancer in the colon ; Colon Carcinoma ; Reporting ; assisted living ; assistive living ; assistive living facilities ; Assisted Living Facilities ; Adverse Experience ; Adverse event ; Documentation ; vulnerable group ; Vulnerable Populations ; Renal/Urologic Organ System ; Urologic/Renal Body System ; Urinary system ; Data ; Clinical Management ; Patient-Focused Outcomes ; Patient outcome ; Patient-Centered Outcomes ; Monitor ; Characteristics ; Health Professional ; Health Care Professional ; Healthcare professional ; Image ; imaging ; caregiving ; care giving ; cost ; digital ; community living ; Population ; Prevalence ; Early treatment ; early therapy ; Computational algorithm ; computer algorithm ; stem ; Biological Markers ; bio-markers ; biologic marker ; biomarker ; resistant strain ; resistance strain ; clinical decision-making ; screening ; healthcare-associated infections ; health care-associated infections ; machine learning algorithm ; machine learned algorithm ; memory care ; infection rate ; rate of infection ; patient health information ; patient health record ; patient medical record ; detection method ; detection procedure ; detection technique ;
