[{"data":1,"prerenderedAt":725},["ShallowReactive",2],{"uc-outbound-post-discharge-follow-up-agent":3,"uc-regulations":506},{"useCase":4,"evidence":221,"blitsAiDeployments":394,"benchmarks":395,"indicative":420,"related":423,"indexability":504,"includeUnpublished":227},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"problem":33,"problemStats":34,"howItWorks":44,"valueDrivers":45,"kpis":50,"indicativeValue":55,"macroEstimates":97,"feasibility":98,"implementation":112,"risk":163,"blitsAi":198,"faq":200,"related":213,"datePublished":216,"dateModified":216,"lastVerified":216,"changelog":217,"slug":220},"AI agent for outbound post discharge and recovery follow up calls","Post discharge follow up calls","AI post discharge follow up calls for patients","AI agents phone patients after discharge or surgery, check recovery and flag concerns for a nurse. Oxford University Hospitals and UHS use them for follow up calls.","published","An AI post discharge follow up agent phones patients after discharge or a procedure, asks clinician approved questions about their recovery and flags anyone who needs a clinical review. Nurses and doctors read the call reports and call back the flagged patients, so routine check ins no longer take clinical time for every patient.",[12,13,14,15,16],"AI discharge follow up calls","post operative AI telephone follow up","automated patient check in calls","AI recovery monitoring calls","post procedure telephone assessment",[18],"healthcare",[20,21],"operations","customer-service",[23,24,25,26],"voice-agent","conversational-agent","agentic-workflow","classification-and-routing",[28,29],"voice","sms","customer-facing","supervised-agent","early-adopters","A patient who goes home after surgery or a hospital stay is at a moment of real risk and little\ncontact. Hospitals follow up in different ways. Some units have a clinician phone each patient, while\nothers discharge uncomplicated patients with advice on warning symptoms and ask them to contact the\nhospital if these occur. The authors of an Oxford and Buckinghamshire evaluation note that each of\nthese pathways either needs trained clinical staff time or depends on the patient making contact,\nwhich may delay the finding of a complication, and that finding enough staff to make follow up calls\nis challenging.\n\nMost patients recover without a problem, which is what makes the check repetitive. In that\nevaluation, 63% of the patients who answered the AI call had no clinical concern. The few who do need\nattention are found by asking the same short list of questions each time. A voice agent can ask them\non a set schedule, try again when nobody answers, and give the clinician a written\nreport. In the United States, the Medicare Hospital Readmissions Reduction Program adds a payment\nreason to stay in touch after discharge.",[35,40],{"statement":36,"sourceTitle":37,"sourceUrl":38,"year":39},"CMS says the payment adjustment under its Hospital Readmissions Reduction Program ranges from 0.97, a maximum payment reduction of 3 percent, to 1.0, which means no reduction.","Hospital Readmissions Reduction Program","https:\u002F\u002Fwww.cms.gov\u002Fmedicare\u002Fquality\u002Fvalue-based-programs\u002Fhospital-readmissions",2026,{"statement":41,"sourceTitle":42,"sourceUrl":43,"year":39},"A Clinical Ophthalmology evaluation of an automated cataract follow up call cites an estimated 20 million cataract procedures performed worldwide each year, with volumes projected to rise as populations age.","Evaluation of Routine Clinical Deployment of an Autonomous Artificial Intelligence Assistant for Cataract Follow-Up in the National Health Service","https:\u002F\u002Fwww.dovepress.com\u002Fevaluation-of-routine-clinical-deployment-of-an-autonomous-artificial--peer-reviewed-fulltext-article-OPTH","1. **The care team lists the patient.** Each pathway has inclusion criteria, such as an adult with an\n   uncomplicated procedure who can talk on the phone in a language the agent supports. At Oxford\n   University Hospitals and Buckinghamshire Healthcare, a clinician requests the call on the surgery\n   listing or the operation note, and patients with other eye conditions stay on the usual pathway.\n2. **The agent calls at a set time after the procedure.** The timing follows the clinical protocol.\n   Dora calls three weeks after cataract surgery. LOLA called valve replacement patients at Dr Balmis\n   General University Hospital at weeks 1, 2 and 4 and months 3 and 12. If nobody answers, Dora tries\n   another time on any listed number, and Milton Keynes University Hospital texts patients the time of\n   the call.\n3. **Design the opening so the patient knows who is calling.** We recommend that the agent names the\n   hospital, says it is an automated assistant and checks that it is speaking to the patient before it\n   asks about their health. The sources on this page do not describe this step, and the guardrails\n   below require it.\n4. **It asks the protocol questions.** The questions cover symptoms, medication and discharge\n   instructions, and the patient can ask questions of their own. Universal Health Services reviews\n   medication instructions and asks about new or worsening symptoms. Dora also records a preference\n   for second eye surgery.\n5. **Rules classify the outcome.** In the Oxford and Buckinghamshire evaluation, red or painful eyes, new floaters or flashing lights,\n   vision concerns and questions the agent cannot answer give a \"potential concerns\" outcome, and\n   everything else is \"no concerns identified\". Clinical teams set these rules.\n6. **A report reaches a clinician.** The clinician reads the summary, discharges the patient or lists\n   them for the next step, and calls back patients with potential concerns, within 48 hours at\n   Oxford and Buckinghamshire. At Summerlin Hospital the system pushes a message to the nurse when a patient wants a call\n   back.\n7. **A person checks a sample.** Trained reviewers check recorded calls against what the agent\n   reported. At Oxford and Buckinghamshire at least 10% of calls were quality assured at the time of\n   the evaluation.",[46,47,48,49],"employee-productivity","risk-reduction","customer-experience","cost-to-serve",[51,52,53,54],"interactions-handled","accuracy","customer-satisfaction","cost-savings",{"referenceOrg":56,"inputs":57,"formula":92,"currency":93,"period":94,"resultLabel":95,"caveat":96},"A hospital group that discharges 40,000 patients a year into a routine follow up call",[58,64,71,78,85],{"key":59,"label":60,"low":61,"high":61,"unit":62,"note":63},"patients","Patients discharged each year who are due a follow up call",40000,"patients per year","The reference organization.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"eligible","Share of those patients the agent can call",0.5,0.9,"fraction of patients","Editorial assumption. At Oxford University Hospitals and Buckinghamshire Healthcare 97% of patients listed for a Dora call were eligible, but that is one narrow pathway; replace with the share for your own pathways.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"handled","Share of called patients that need no clinician time beyond reading the report",0.35,0.48,"fraction of called patients","Based on the Oxford and Buckinghamshire evaluation, where 767 of the 1580 eligible patients Dora contacted (48.5%) had no clinical concerns. The 63% figure in that paper is relative to the 1213 patients who completed the call, and patients who did not answer still cost clinician time because their notes were assessed. The low value allows for pathways with less reach. Replace with your own.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"minutes","Nurse time a manual follow up call takes",10,15,"minutes per patient","Editorial assumption, replace with your own time study.",{"key":86,"label":87,"low":88,"high":89,"unit":90,"note":91},"hourlyCost","Fully loaded cost of nurse time",35,60,"EUR per hour","Editorial assumption, replace with your own cost.","patients * eligible * handled * minutes \u002F 60 * hourlyCost","EUR","per year","Clinician time released from routine follow up calls","Counts nurse time saved on patients with no concern only. It leaves out the cost of the AI, the telephony and the quality review, the extra callbacks that false flags create, and any effect on readmissions or complications, which none of the deployments on this page report.",[],{"complexity":99,"complexityNote":100,"dataPrerequisites":101,"integrations":106},"high","The build needs a feed of discharged and operated patients from the electronic health record or patient administration system, call protocols signed off by clinicians, a written rule for what counts as a concern, a worklist where a clinician reads each report within a fixed time, and a quality review of recorded calls. In the UK and the EU, software that classifies symptoms can count as a medical device, as it does for Dora and LOLA, which adds a conformity route and post market surveillance.",[102,103,104,105],"Daily list of discharged or operated patients with procedure, date, contact numbers, language and consent","Clinician approved question protocols and escalation rules per pathway","Inclusion and exclusion criteria per pathway","Outcome data for the safety audit, such as unplanned visits, treatment changes and readmissions within a set window",[107,108,109,110,111],"Electronic health record or patient administration system (read and write)","Telephony and SMS provider","Clinician worklist or task queue for call reports and callbacks","Nurse line or contact center for handover","Call recording and quality review tool",{"steps":113,"guardrails":132,"humanInTheLoop":138,"kpisToInstrument":139,"failureModes":147},[114,117,120,123,126,129],{"title":115,"detail":116},"Start with one routine pathway","Pick a pathway where most patients recover without events and a clinician already follows a script, as Oxford and Buckinghamshire did with uncomplicated cataract surgery. Write the inclusion criteria first and keep patients with complicating conditions on the existing route.",{"title":118,"detail":119},"Set the flag rule to miss almost nothing","Agree with clinicians that doubt counts as a concern. The Oxford and Buckinghamshire evaluation reports a positive predictive value of 16.8% for \"potential concerns\" and a negative predictive value of 99.5% for \"no concerns\", described by its authors as an intentionally cautious design. Expect many flags that turn out fine and size the callback team for them.",{"title":121,"detail":122},"Decide what happens to every outcome","Write a rule for no concerns, potential concerns, no answer and an incomplete call, with a time limit for each callback. At Oxford and Buckinghamshire patients with potential concerns are called back within 48 hours, and patients who do not answer are handled like a missed clinic booking.",{"title":124,"detail":125},"Audit safety against what happens next","Compare patients with a \"no concerns\" outcome against unplanned visits and treatment changes in a set window after the call. The Oxford and Buckinghamshire evaluation used three months and traced four unexpected management changes within two weeks of such calls.",{"title":127,"detail":128},"Check who the agent cannot reach","Report reach and outcomes by age, language and ethnicity. The Oxford and Buckinghamshire evaluation found no significant difference in call outcomes by age, gender, ethnicity or eye status, but only for patients who met the inclusion criteria, and 3% of listed patients were ineligible, mostly for complex surgery, language, cognitive or hearing reasons. Keep a human route for those patients.",{"title":130,"detail":131},"Add pathways one at a time","Move to the next procedure only after a clinician has reviewed a sample of calls and the audit shows no missed concerns. Each pathway needs its own protocol and its own flag rule.",[133,134,135,136,137],"Clinicians own the question set and the flag rules, and the agent never reassures beyond its script","A question the agent cannot answer counts as a concern, not as no concern","The agent discloses that it is automated and verifies the patient before asking about health","Patients can ask for a person at any point on the call","Patients with an urgent clinical concern or a communication need are kept on the human route","A named clinician reads every report that carries a flag and decides the next step, and a sample of \"no concerns\" reports is read as well. A quality reviewer may raise a flag but should not clear one without a clinician seeing the call, and unanswered calls are assessed against the patient record instead of being discharged by default.",[140,141,142,143,144,145,146],"Answer rate and completed call rate per pathway","Share of calls flagged, and the share of flags a clinician confirms","Unplanned visits and treatment changes after a \"no concerns\" outcome","Time from flag to clinician callback","Patient rating and the share who ask for a person","Clinician minutes per patient followed up","Reach and outcomes by age and language",[148,151,154,157,160],{"title":149,"detail":150},"A flag cleared by a reviewer","In the Oxford and Buckinghamshire evaluation the call of a patient with a retained lens fragment was flagged by Dora as potential concerns, and the human quality assurance step relabeled it as \"no concerns\". The patient later needed an unexpected change in management. Reviewers who can lower a flag need a clinician to see the call first.",{"title":152,"detail":153},"Callbacks swamp the clinic","A cautious rule produces many flags. With a positive predictive value of 16.8%, most callbacks end with reassurance. Track flag volume against callback capacity and tune the questions with clinicians.",{"title":155,"detail":156},"Reach gaps nobody sees","Patients without a phone, with hearing or cognitive difficulty, or who speak another language drop out of the automated route, and an average hides it. Report reach by group and keep a manual route.",{"title":158,"detail":159},"A satisfaction score with no method","A rating such as 9.0 out of 10 says little without the number of responses and the question asked. Record the method before the score is quoted to a board or a regulator.",{"title":161,"detail":162},"Drift into triage and treatment advice","Once the agent judges symptoms, it can become a regulated medical device. Keep scripts inside the protocol and check the device rules in your market before widening the scope.",{"euAiAct":164,"regulations":167,"guidance":173,"controls":191,"incidents":197},{"tier":165,"basis":166},"context-dependent","A voice agent that talks to patients must tell them they are interacting with an AI system (Article 50). If the software is itself a medical device, or a safety component of one, that needs a third party conformity assessment under the EU Medical Device Regulation, it is high risk under Article 6(1) and Annex I. A script that only collects answers for a clinician to read is closer to limited risk, while a system that classifies symptoms and decides who is reviewed can move into device territory. Dora is described as a UKCA medical device and LOLA as a class I medical device under the earlier EU directive.",[168,169,170,171,172],"eu-ai-act","gdpr","uk-gdpr","hipaa","us-tcpa",[174,180,186],{"title":175,"issuer":176,"region":177,"url":178,"note":179},"Declaratory ruling on AI generated voices under the TCPA (FCC 24-17)","Federal Communications Commission","north-america","https:\u002F\u002Fdocs.fcc.gov\u002Fpublic\u002Fattachments\u002FFCC-24-17A1.pdf","AI generated voices count as an artificial or prerecorded voice under the TCPA, so US follow up calls made with AI need the consent the TCPA requires for such calls.",{"title":181,"issuer":182,"region":183,"url":184,"note":185},"Medical devices, software applications (apps)","Medicines and Healthcare products Regulatory Agency","europe","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fpublications\u002Fmedical-devices-software-applications-apps","Explains when stand alone software counts as a medical device in Great Britain, which decides whether a symptom classifying follow up agent needs UKCA marking.",{"title":187,"issuer":188,"region":183,"url":189,"note":190},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","People must be informed that they are interacting with an AI system unless this is obvious from the context.",[192,193,194,195,196],"Clinical sign off of every question protocol and flag rule, with a version history","Written decision on whether the software is a medical device in each market it is used","Call recordings and reports kept under the health record retention policy, with access logs","Safety audit of \"no concerns\" outcomes against unplanned care, reviewed by a clinical lead","Consent and opt out for calls and texts, logged per patient",[],{"howToBuild":199},"On Blits.ai each follow up pathway is an **agentic workflow** started on a schedule or by the\npatient administration system through an API token, with the patient, procedure and call time passed\nin as data. A **custom function** calls the telephony or messaging provider to start the contact,\nand an **agentic task** can recheck later and try again when a call went unanswered. The\nconversation runs on a **voice** channel with streaming speech recognition and synthesis, with\n**SMS** for the heads up text. A **flow** fixes the opening (who is calling and that it is an\nautomated assistant), the identity check and the clinician approved questions, and an **AI agent**\nhandles the patient's own questions against a **knowledge base** that holds only the approved\ndischarge instructions.\n\nCustom functions write the outcome and the call summary back to the clinical system and put flagged\npatients on a clinician worklist. **Guardrails** keep the agent inside the protocol, **PII masking**\nprotects patient details in logs and prompts, and **human handover** with live conversation\nmonitoring lets a nurse take over a call. **Test suites** replay scripted recovery conversations\non every change, **monitors** and **analytics** show answer rates and flag rates per pathway, and\n**EU and UAE data residency** keeps the data in region. The platform is model agnostic, so the\nhospital can choose or switch the model per agent.",[201,204,207,210],{"question":202,"answer":203},"Do patients accept an AI follow up call?","The deployments report acceptance, though by different measures. At Oxford University Hospitals and Buckinghamshire Healthcare 86% of eligible patients answered the Dora call, and the patient acceptability Net Promoter Score was 47. At Dr Balmis General University Hospital 88.9% of patients said they were satisfied or very satisfied, and UHS reports an average rating of 9.0 out of 10. Newcastle University's earlier trial found most participants accepting, with some concern about the lack of a human element when complications arise.",{"question":205,"answer":206},"Does an AI follow up call reduce readmissions?","None of the deployments on this page reports a readmission result from the calls themselves. The Alicante study found no significant difference in complications or readmissions between patients discharged very early, early or on the standard timeline, but it had no control group without the assistant. Treat readmission claims from vendors without a named hospital and a method with caution.",{"question":208,"answer":209},"Is a post discharge follow up agent a medical device?","It can be. Dora is described as a UKCA medical device and LOLA as a class I medical device under the earlier EU directive. A script that collects answers for a clinician to read is a different product from one that decides who is flagged, so ask your regulator which applies before you launch.",{"question":211,"answer":212},"Which patients should stay on the human route?","Patients with an urgent concern, complex or combined procedures, and patients who cannot hold a phone conversation in a supported language or have a hearing or cognitive impairment. At Oxford and Buckinghamshire 3% of patients listed for a Dora call were ineligible, mostly for these reasons.",[214,215],"patient-appointment-scheduling-and-reminders-agent","outbound-reminder-and-confirmation-agent","2026-10-14",[218],{"date":216,"note":219},"First published","outbound-post-discharge-follow-up-agent",[222,261,301,341,373],{"title":223,"useCases":224,"organization":225,"vendors":230,"summary":234,"stage":235,"year":236,"channels":237,"languages":238,"metrics":240,"outcomeDisclosed":241,"sources":242,"verification":256,"grade":258,"id":259,"organizationSlug":260},"Universal Health Services: Hippocratic AI voice agents call patients after discharge",[220],{"name":226,"anonymized":227,"country":228,"region":177,"industry":18,"wikidata":229},"Universal Health Services",false,"US","Q7893967",[231],{"name":232,"role":233},"Hippocratic AI","platform","Universal Health Services (UHS) and Hippocratic AI deployed generative AI agents that phone patients after they leave hospital, starting at Summerlin Hospital Medical Center in Las Vegas and Texoma Medical Center in Denison, Texas. The agent reviews discharge and medication instructions, asks about new or worsening symptoms and answers questions, and the system pushes a message to the nurse when a patient wants a call back. UHS says thousands of patients were contacted from the start of 2025 and reports an average patient rating of 9.0 out of 10, with plans to extend the program to more of its acute care hospitals.","production",2025,[28],[239],"en",[],true,[243,247,252],{"url":244,"title":245,"publisher":226,"date":246},"https:\u002F\u002Fuhs.com\u002Fnews\u002Funiversal-health-services-launches-hippocratic-ais-generative-ai-healthcare-agents-to-assist-with-post-discharge-patient-engagement\u002F","Universal Health Services Launches Hippocratic AI's Generative AI Healthcare Agents to Assist with Post-Discharge Patient Engagement","2025-06-16",{"url":248,"title":249,"publisher":250,"date":251},"https:\u002F\u002Fsouthfloridahospitalnews.com\u002F2025\u002F06\u002F20\u002Funiversal-health-services-launches-hippocratic-ais-generative-ai-healthcare-agents-to-assist-with-post-discharge-patient-engagement\u002F","Universal Health Services Launches Hippocratic AI's Generative AI Healthcare Agents to Assist with Post-Discharge Patient Engagement (press release copy)","Florida Hospital News and Healthcare Report","2025-06-20",{"url":253,"title":254,"publisher":255},"https:\u002F\u002Fwww.healthcaredive.com\u002Fnews\u002Fuhs-partners-hippocratic-ai-launch-ai-agents\u002F750892\u002F","UHS partners with Hippocratic AI to launch AI agents","Healthcare Dive",{"level":257,"checkedAt":216},"source-verified","B","universal-health-services-hippocratic-ai-post-discharge-calls",null,{"title":262,"useCases":263,"organization":264,"vendors":267,"summary":270,"stage":271,"year":272,"channels":273,"languages":274,"metrics":276,"outcomeDisclosed":241,"sources":290,"verification":299,"grade":258,"id":300,"organizationSlug":260},"Dr Balmis General University Hospital: LOLA voice assistant for follow up after heart valve replacement",[220],{"name":265,"anonymized":227,"country":266,"region":183,"industry":18},"Dr Balmis General University Hospital","ES",[268],{"name":269,"role":233},"Tucuvi","The cardiology department of Dr Balmis General University Hospital in Alicante enrolled 274 of 302 screened patients discharged after transcatheter aortic valve implantation in 2023 in the TeleTAVI study (28 were excluded for language, access route or consent), in which Tucuvi's voice assistant LOLA phoned patients at weeks 1, 2 and 4 and months 3 and 12 using clinically designed question protocols. Calls that raised alerts went to the clinical team, and 73% of the 274 patients went home within 48 hours. The authors report no significant differences in complications or readmissions between very early, early and standard discharge. It was a single center, single arm study.","pilot",2023,[28],[275],"es",[277,285],{"kpi":51,"value":278,"unit":279,"qualifier":280,"period":281,"claimant":282,"quote":283,"sourceUrl":284},1039,"count","exact","calls to the 274 patients enrolled in the TeleTAVI study in 2023, with follow up scheduled up to 12 months","organization","A total of 1039 calls were made using the virtual assistant, with a total of 385 h of automated conversation.","https:\u002F\u002Fwww.ebi.ac.uk\u002Feuropepmc\u002Fwebservices\u002Frest\u002FPMC11750190\u002FfullTextXML",{"kpi":53,"value":286,"unit":287,"qualifier":280,"period":288,"claimant":282,"quote":289,"sourceUrl":284},88.9,"percent","patients satisfied or very satisfied, CSAT 4.68 of 5","The vast majority of patients (88.9%) reported to be satisfied or very satisfied (CSAT 4.68\u002F5); 86.3% of them would recommend the virtual assistant, ‘Lola’, according to the ‘NPS’ scale.",[291,296],{"url":292,"title":293,"publisher":294,"date":295},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC11750190\u002F","Early discharge programme after transcatheter aortic valve implantation based on close follow-up supported by telemonitoring using artificial intelligence: the TeleTAVI study (open access full text)","European Heart Journal, Digital Health (PubMed Central)","2024-11-20",{"url":284,"title":297,"publisher":298,"date":295},"TeleTAVI study, full text XML","Europe PMC",{"level":257,"checkedAt":216},"dr-balmis-general-university-hospital-tucuvi-lola-tavi-follow-up",{"title":302,"useCases":303,"organization":304,"vendors":308,"summary":311,"stage":235,"year":312,"channels":313,"languages":314,"metrics":315,"outcomeDisclosed":241,"sources":331,"verification":339,"grade":258,"id":340,"organizationSlug":260},"Oxford University Hospitals: Dora phones cataract patients for post operative follow up",[220],{"name":305,"anonymized":227,"country":306,"region":183,"industry":18,"wikidata":307},"Oxford University Hospitals NHS Foundation Trust","GB","Q16976147",[309],{"name":310,"role":233},"Ufonia","Oxford University Hospitals and Buckinghamshire Healthcare NHS Trust use Ufonia's Dora, an automated telephone assistant, as the standard follow up for patients who had routine cataract surgery. Dora calls three weeks after surgery, asks symptom questions, takes second eye surgery preferences and hands a report to a clinician, who discharges the patient or lists them for second eye surgery, or calls back within 48 hours when the report shows potential concerns. A peer reviewed service evaluation of the first seven months at each site covered 1636 patients. An earlier trial led by Newcastle University with Oxford and Imperial College Healthcare estimated a saving of around 35 pounds per patient.",2022,[28,29],[239],[316,320,324],{"kpi":51,"value":317,"unit":279,"qualifier":280,"period":318,"claimant":282,"quote":319,"sourceUrl":43},1636,"first seven months of use at each of two NHS trusts","Across both clinical sites, 1636 Dora R2 consultations were included.",{"kpi":52,"value":321,"unit":287,"qualifier":280,"period":322,"claimant":282,"quote":323,"sourceUrl":43},99.5,"negative predictive value of a \"no concerns\" outcome, 763 of 767 patients","The negative predictive value for a “no concerns” outcome was 99.5% (763\u002F767), indicating that when Dora R2 identified no concerns, patients were highly unlikely to require clinical intervention.",{"kpi":54,"value":88,"unit":325,"currency":326,"qualifier":327,"period":328,"claimant":282,"quote":329,"sourceUrl":330},"currency","GBP","approximately","per patient, compared with standard care, estimated in the 2024 trial","Using this telemedicine follow-up assessment was estimated to have saved around £35 per patient compared to standard care.","https:\u002F\u002Fwww.ncl.ac.uk\u002Fpress\u002Farticles\u002Farchive\u002F2024\u002F07\u002Faicataractcarepathway",[332,335],{"url":43,"title":42,"publisher":333,"date":334},"Clinical Ophthalmology (Dove Medical Press)","2026-04-17",{"url":330,"title":336,"publisher":337,"date":338},"AI assistant shows great promise in cataract care pathway","Newcastle University","2024-07-10",{"level":257,"checkedAt":216},"oxford-university-hospitals-ufonia-dora-cataract-follow-up",{"title":342,"useCases":343,"organization":344,"vendors":349,"summary":352,"stage":235,"year":353,"channels":354,"languages":355,"metrics":357,"outcomeDisclosed":241,"sources":363,"verification":371,"grade":258,"id":372,"organizationSlug":260},"Peking Union Medical College Hospital: AI assisted telephone follow up for orthopedic surgery patients",[220],{"name":345,"anonymized":227,"country":346,"region":347,"industry":18,"wikidata":348},"Peking Union Medical College Hospital","CN","asia-pacific","Q7161058",[350],{"name":345,"role":351},"in-house","The orthopedic department of Peking Union Medical College Hospital (PUMCH) adopted an AI assisted follow up system in April 2019 that phones patients after surgery in daily batches, recognizes their speech and dialects, asks scripted questions about recovery, nursing, health education and the ward environment, and passes feedback to surgeons and nurses. A published exploratory study compared 270 patients followed up by the system with 2656 followed up by manual calls. Reach was similar (92.2% connected for the AI system against 93.3% for manual calls), and the AI calls collected feedback from 10.3% of patients against 2.5%.",2019,[28],[356],"zh",[358],{"kpi":51,"value":359,"unit":279,"qualifier":280,"period":360,"claimant":282,"quote":361,"sourceUrl":362},270,"patients followed up by the AI assisted system, April to May 2019","A total of 270 patients were followed up through this system.","https:\u002F\u002Fwww.ebi.ac.uk\u002Feuropepmc\u002Fwebservices\u002Frest\u002FPMC7284488\u002FfullTextXML",[364,369],{"url":365,"title":366,"publisher":367,"date":368},"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC7284488\u002F","Artificial Intelligence-Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study (open access full text)","Journal of Medical Internet Research (PubMed Central)","2020-05-26",{"url":362,"title":370,"publisher":298,"date":368},"AI assisted postoperative follow up study, full text XML",{"level":257,"checkedAt":216},"peking-union-medical-college-hospital-ai-orthopedic-follow-up",{"title":374,"useCases":375,"organization":376,"vendors":378,"summary":380,"stage":271,"year":381,"channels":382,"languages":383,"metrics":384,"outcomeDisclosed":227,"sources":385,"verification":391,"grade":392,"id":393,"organizationSlug":260},"Milton Keynes University Hospital: Dora trial for post cataract follow up calls",[220],{"name":377,"anonymized":227,"country":306,"region":183,"industry":18},"Milton Keynes University Hospital",[379],{"name":310,"role":233},"The ophthalmology department at Milton Keynes University Hospital started a 12 month trial of Dora, Ufonia's conversational AI assistant, in July 2024. Dora calls patients after cataract surgery to ask about symptoms and whether they want their second eye operated on, answers common questions and collects feedback on the service. Patients with a mobile phone get a text with the date and time of the call, and Dora tries other times and days when the first does not suit. No results are reported yet.",2024,[28,29],[239],[],[386],{"url":387,"title":388,"publisher":389,"date":390},"https:\u002F\u002Fhealthbusinessuk.net\u002Fnode\u002F7937","Hospital trials AI assistant to help with follow-up appointments","Health Business","2024-08-05",{"level":257,"checkedAt":216},"C","milton-keynes-university-hospital-ufonia-dora-ophthalmology",0,[396,404,410,415],{"kpi":51,"label":397,"unit":279,"aggregate":227,"higherIsBetter":241,"n":398,"nUpTo":394,"median":278,"min":359,"max":317,"byClaimant":399,"vendorOnly":227,"points":400},"Interactions handled",3,{"organization":398,"vendor":394,"regulator":394,"independent":394},[401,402,403],{"evidenceId":340,"organization":305,"value":317,"qualifier":280,"claimant":282,"grade":258,"pooled":241},{"evidenceId":300,"organization":265,"value":278,"qualifier":280,"claimant":282,"grade":258,"pooled":241},{"evidenceId":372,"organization":345,"value":359,"qualifier":280,"claimant":282,"grade":258,"pooled":241},{"kpi":52,"label":405,"unit":287,"aggregate":241,"higherIsBetter":241,"n":406,"nUpTo":394,"median":321,"min":321,"max":321,"byClaimant":407,"vendorOnly":227,"points":408},"Accuracy",1,{"organization":406,"vendor":394,"regulator":394,"independent":394},[409],{"evidenceId":340,"organization":305,"value":321,"qualifier":280,"claimant":282,"grade":258,"pooled":241},{"kpi":54,"label":411,"unit":325,"currency":326,"aggregate":227,"higherIsBetter":241,"n":406,"nUpTo":394,"median":88,"min":88,"max":88,"byClaimant":412,"vendorOnly":227,"points":413},"Cost savings",{"organization":406,"vendor":394,"regulator":394,"independent":394},[414],{"evidenceId":340,"organization":305,"value":88,"qualifier":327,"claimant":282,"grade":258,"pooled":241},{"kpi":53,"label":416,"unit":287,"aggregate":241,"higherIsBetter":241,"n":406,"nUpTo":394,"median":286,"min":286,"max":286,"byClaimant":417,"vendorOnly":227,"points":418},"Customer satisfaction",{"organization":406,"vendor":394,"regulator":394,"independent":394},[419],{"evidenceId":300,"organization":265,"value":286,"qualifier":280,"claimant":282,"grade":258,"pooled":241},{"low":421,"high":422},40833.333333333336,259200,[424,451,465,482],{"slug":214,"title":425,"shortTitle":426,"definition":427,"status":9,"industries":428,"functions":429,"patterns":430,"audience":30,"autonomy":31,"adoptionStage":32,"evidenceCount":432,"publicEvidenceCount":433,"organizations":434,"bestGrade":258,"headline":444,"lastVerified":450,"indexable":241},"AI agent for patient appointment scheduling, reminders and no show reduction","Patient scheduling and reminders","An AI agent for patient scheduling and reminders books, moves and cancels patient appointments by phone and messaging, and reminds and confirms patients. It follows the provider's scheduling rules (referral, triage level, clinician and visit type, preparation), confirms and reminds patients in two way conversations, predicts who is likely to miss an appointment, and offers freed slots to patients on the waiting list. Unlike a general branch and appointment booking agent, it writes into the electronic health record and must respect clinical constraints, so anything clinical goes to staff.",[18],[21,20],[23,24,431,25],"prediction-and-scoring",12,9,[435,436,437,438,439,440,441,442,443],"Audibel","Good Shepherd Rehabilitation Network","Howard Brown Health","Mid and South Essex NHS Foundation Trust","North Kansas City Hospital","Reid Health","Sheffield Children's NHS Foundation Trust","University Hospitals Coventry and Warwickshire NHS Trust","WellSpan Health",{"kpi":445,"label":446,"unit":287,"n":406,"nUpTo":394,"kind":447,"value":448,"qualifier":280,"claimant":449,"organization":437,"vendorReported":241},"containment-rate","Containment rate","reported",30,"vendor","2026-09-27",{"slug":215,"title":452,"shortTitle":453,"definition":454,"status":9,"industries":455,"functions":458,"patterns":459,"audience":30,"autonomy":31,"adoptionStage":32,"evidenceCount":460,"publicEvidenceCount":461,"organizations":462,"bestGrade":258,"headline":260,"lastVerified":464,"indexable":241},"AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent for outbound reminders and confirmations contacts customers about something they already booked or ordered, to remind them and confirm attendance. That can be an appointment, a delivery, a reservation or a service visit, and the agent lets them cancel or move it in the same conversation, by phone, SMS, WhatsApp or email. This is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion.",[456,18,457],"cross-industry","government",[21,20],[23,24,25,431],5,4,[441,442,463,443],"U.S. Department of Veterans Affairs","2026-09-26",{"slug":466,"title":467,"shortTitle":468,"definition":469,"status":9,"industries":470,"functions":473,"patterns":475,"audience":30,"autonomy":31,"adoptionStage":32,"segment":477,"evidenceCount":461,"publicEvidenceCount":398,"organizations":478,"bestGrade":258,"headline":260,"lastVerified":450,"indexable":241},"card-dispute-and-chargeback-intake","AI agent for card dispute intake","Card dispute intake","Card dispute intake is a customer facing AI agent that finds a disputed card transaction and separates suspected fraud from merchant disputes and simple confusion. It explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.",[471,472],"banking","payments",[21,474,20],"fraud-prevention",[24,23,26,476,25],"document-processing","front-office",[479,480,481],"Commonwealth Bank of Australia","Klarna","Visa",{"slug":483,"title":484,"shortTitle":485,"definition":486,"status":9,"industries":487,"functions":489,"patterns":490,"audience":30,"autonomy":31,"adoptionStage":32,"evidenceCount":491,"publicEvidenceCount":492,"organizations":493,"bestGrade":258,"headline":501,"lastVerified":450,"indexable":241},"parcel-tracking-and-delivery-exception-agent","AI agent for parcel tracking and delivery exceptions","Parcel tracking and delivery exceptions","An AI agent for parcel tracking and delivery exceptions answers \"where is my parcel\" and resolves delivery exceptions for parcel carriers and postal operators. It handles issues such as missed deliveries, redelivery or a change of address or pickup point, delays, customs holds and lost or damaged parcel claims, on chat, messaging and phone, and hands disputes and claims above set limits to a human with the tracking history attached.",[488],"logistics-and-transportation",[21,20],[24,23,25,26],8,7,[494,495,496,497,498,499,500],"Aramex","Chronopost","DPD Belgium","DPD Deutschland","DPD UK","Evri","PostNL",{"kpi":445,"label":446,"unit":287,"n":502,"nUpTo":394,"kind":447,"value":503,"qualifier":280,"claimant":282,"organization":495,"vendorReported":227},2,85,{"indexable":241,"reasons":505},[],[507,511,516,524,531,537,543,550,557,564,570,576,582,588,595,600,606,610,616,623,628,633,639,645,652,657,662,667,673,678,686,692,697,703,709,714,719],{"id":168,"label":508,"issuer":188,"region":183,"url":189,"description":509,"useCases":510,"indexable":241},"EU AI Act","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",295,{"id":169,"label":512,"issuer":188,"region":183,"url":513,"description":514,"useCases":515,"indexable":241},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",265,{"id":517,"label":518,"issuer":519,"region":520,"url":521,"description":522,"useCases":523,"indexable":241},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",148,{"id":525,"label":526,"issuer":527,"region":177,"url":528,"description":529,"useCases":530,"indexable":241},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",115,{"id":170,"label":532,"issuer":533,"region":183,"url":534,"description":535,"useCases":536,"indexable":241},"UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",84,{"id":538,"label":539,"issuer":188,"region":183,"url":540,"description":541,"useCases":542,"indexable":241},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",73,{"id":544,"label":545,"issuer":546,"region":183,"url":547,"description":548,"useCases":549,"indexable":241},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",53,{"id":551,"label":552,"issuer":553,"region":347,"url":554,"description":555,"useCases":556,"indexable":241},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":558,"label":559,"issuer":560,"region":347,"url":561,"description":562,"useCases":563,"indexable":241},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":565,"label":566,"issuer":188,"region":183,"url":567,"description":568,"useCases":569,"indexable":241},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":571,"label":572,"issuer":573,"region":520,"url":574,"description":575,"useCases":569,"indexable":241},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":577,"label":578,"issuer":579,"region":177,"url":580,"description":581,"useCases":569,"indexable":241},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":171,"label":583,"issuer":584,"region":177,"url":585,"description":586,"useCases":587,"indexable":241},"HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",20,{"id":589,"label":590,"issuer":591,"region":183,"url":592,"description":593,"useCases":594,"indexable":241},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":596,"label":597,"issuer":188,"region":183,"url":598,"description":599,"useCases":82,"indexable":241},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":601,"label":602,"issuer":603,"region":520,"url":604,"description":605,"useCases":82,"indexable":241},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":172,"label":607,"issuer":176,"region":177,"url":608,"description":609,"useCases":82,"indexable":241},"Telephone Consumer Protection Act","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":611,"label":612,"issuer":188,"region":183,"url":613,"description":614,"useCases":615,"indexable":241},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":617,"label":618,"issuer":619,"region":177,"url":620,"description":621,"useCases":622,"indexable":241},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":624,"label":625,"issuer":188,"region":183,"url":626,"description":627,"useCases":622,"indexable":241},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":629,"label":630,"issuer":188,"region":183,"url":631,"description":632,"useCases":622,"indexable":241},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":634,"label":635,"issuer":636,"region":520,"url":637,"description":638,"useCases":622,"indexable":241},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":640,"label":641,"issuer":642,"region":177,"url":643,"description":644,"useCases":432,"indexable":241},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":646,"label":647,"issuer":648,"region":183,"url":649,"description":650,"useCases":651,"indexable":241},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":653,"label":654,"issuer":188,"region":183,"url":655,"description":656,"useCases":651,"indexable":241},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":658,"label":659,"issuer":188,"region":183,"url":660,"description":661,"useCases":651,"indexable":241},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":663,"label":664,"issuer":553,"region":347,"url":665,"description":666,"useCases":81,"indexable":241},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":668,"label":669,"issuer":670,"region":177,"url":671,"description":672,"useCases":491,"indexable":241},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":674,"label":675,"issuer":188,"region":183,"url":676,"description":677,"useCases":491,"indexable":241},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":679,"label":680,"issuer":681,"region":682,"url":683,"description":684,"useCases":685,"indexable":241},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",6,{"id":687,"label":688,"issuer":689,"region":183,"url":690,"description":691,"useCases":460,"indexable":241},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":693,"label":694,"issuer":188,"region":183,"url":695,"description":696,"useCases":461,"indexable":241},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":698,"label":699,"issuer":700,"region":183,"url":701,"description":702,"useCases":461,"indexable":241},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":704,"label":705,"issuer":706,"region":347,"url":707,"description":708,"useCases":398,"indexable":241},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":710,"label":711,"issuer":188,"region":183,"url":712,"description":713,"useCases":398,"indexable":241},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":715,"label":716,"issuer":553,"region":347,"url":717,"description":718,"useCases":398,"indexable":241},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":720,"label":721,"issuer":722,"region":177,"url":723,"description":724,"useCases":398,"indexable":241},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1792049858758]