[{"data":1,"prerenderedAt":705},["ShallowReactive",2],{"uc-patient-appointment-scheduling-and-reminders-agent":3,"uc-regulations":496},{"useCase":4,"evidence":213,"blitsAiDeployments":368,"benchmarks":369,"indicative":396,"related":399,"indexability":494,"includeUnpublished":219},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":27,"audience":33,"autonomy":34,"adoptionStage":35,"problem":36,"problemStats":37,"howItWorks":48,"valueDrivers":49,"kpis":54,"indicativeValue":60,"macroEstimates":95,"feasibility":100,"implementation":115,"risk":158,"blitsAi":188,"faq":190,"related":203,"datePublished":208,"dateModified":208,"lastVerified":208,"changelog":209,"slug":212},"AI agent for patient appointment scheduling, reminders and no show reduction","Patient scheduling and reminders","AI patient scheduling and appointment reminders","AI agents book, move and remind patients by phone and text. NHS England reports a 30% fall in missed appointments in a Mid and South Essex prediction pilot.","published","An AI agent that books, moves and cancels patient appointments by phone and messaging while following 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.",[12,13,14,15,16],"patient scheduling agent","hospital appointment reminder AI","no show prediction and outreach","AI patient access agent","missed appointment reduction",[18],"healthcare",[20,21],"customer-service","operations",[23,24,25,26],"voice-agent","conversational-agent","prediction-and-scoring","agentic-workflow",[28,29,30,31,32],"voice","sms","web-chat","mobile-app","whatsapp","customer-facing","supervised-agent","early-adopters","Getting a patient into the right slot is harder than booking a table. Appointments depend on a\nreferral, a triage level, the right clinician and room, preparation instructions and sometimes a\ntest that has to happen first. Patient access teams do this by phone, and at peak times callers wait\non hold or give up, so access to care depends on how long someone can stay on the line.\n\nAt the other end, a large share of booked appointments is simply lost. Patients forget, cannot get\ntransport, cannot take time off or no longer need the visit but never cancel. Every missed\nappointment is clinical time that nobody uses while other patients wait. Standard one way text\nreminders help, but they are sent to everyone at the same moment, cannot answer a question and leave\nthe freed slot empty when a patient cancels late.",[38,43],{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":42},"NHS England reports that of 124.5 million outpatient appointments in England in the previous year, eight million (6.4%) were not attended, at an estimated annual cost of £1.2 billion.","NHS AI expansion to help tackle missed appointments and improve waiting times","https://www.england.nhs.uk/2024/03/nhs-ai-expansion-to-help-tackle-missed-appointments-and-improve-waiting-times/",2024,{"statement":44,"sourceTitle":45,"sourceUrl":46,"year":47},"The UK government reports 8 million missed appointments in elective care in 2023 to 2024.","Power to patients as government sets out plan to cut waiting lists","https://www.gov.uk/government/news/power-to-patient-as-government-sets-out-plan-to-cut-waiting-lists",2025,"1. **Take the request on any channel.** The agent answers calls and messages to book, move or cancel\n   an appointment, and identifies the patient against the record before it discloses or changes\n   anything.\n2. **Apply the scheduling rules.** It checks the referral, visit type, clinician, location and any\n   preparation or prior test the rules require, and only offers slots the scheduling system returns\n   for that combination.\n3. **Book and confirm.** It writes the booking into the electronic health record or patient\n   administration system and sends a confirmation with the preparation instructions.\n4. **Remind in a conversation.** Reminders go out at the times that work best for that clinic, and\n   the patient can confirm, cancel or move the appointment in the same thread.\n5. **Target extra help.** A model scores which appointments are likely to be missed; high risk\n   patients get an extra reminder, a better suited time, or an offer of help with transport.\n6. **Backfill freed slots.** When a patient cancels, the agent offers the slot to suitable patients on\n   the waiting list so the clinical time is used.\n7. **Hand clinical questions to people.** Symptoms, urgent concerns and anything outside scheduling\n   go to staff or to urgent care guidance, with the conversation attached.",[50,51,52,53],"customer-experience","cost-to-serve","inclusion-and-access","employee-productivity",[55,56,57,58,59],"containment-rate","interactions-handled","handling-time-reduction","response-time-reduction","customer-satisfaction-uplift",{"referenceOrg":61,"inputs":62,"formula":90,"currency":91,"period":92,"resultLabel":93,"caveat":94},"A hospital with 600,000 outpatient appointments a year",[63,69,76,83],{"key":64,"label":65,"low":66,"high":66,"unit":67,"note":68},"appointments","Outpatient appointments per year",600000,"appointments per year","The reference hospital.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75,"sourceUrl":41},"missedRate","Share of appointments not attended today",0.06,0.08,"fraction of appointments","NHS England reports 6.4% of outpatient appointments in England were not attended, and higher rates in some specialties (11% in physiotherapy, 8.9% in cardiology). Replace with your own rate.",{"key":77,"label":78,"low":79,"high":80,"unit":81,"note":82},"reduction","Share of missed appointments avoided or backfilled",0.1,0.3,"fraction of missed appointments","Conservative against the evidence on this page (NHS England reports a 30% fall in non attendance in the Mid and South Essex pilot). Editorial assumption, replace with your own.",{"key":84,"label":85,"low":86,"high":87,"unit":88,"note":89,"sourceUrl":41},"valuePerSlot","Value of an outpatient slot that is used instead of wasted",120,170,"EUR per appointment","NHS England's estimate of £1.2 billion a year for eight million missed appointments implies roughly £150 (about EUR 175) per missed appointment; the euro range is set below that figure to stay conservative. Replace with your own cost per slot.","appointments * missedRate * reduction * valuePerSlot","EUR","per year","Value of clinical time recovered from avoided or backfilled missed appointments","Counts recovered clinical capacity only. It leaves out booking calls handled by the agent, shorter waits for patients, the cost of the AI, messaging and integration, and the extra appointments that may be needed when more patients attend.",[96],{"statement":97,"sourceTitle":98,"sourceUrl":99,"year":47},"The UK government says focused action, including AI predictions of which appointments are most likely to be missed, will help save up to 1 million missed appointments.","PM sets out plan to end waiting list backlogs through millions more appointments","https://www.gov.uk/government/news/pm-sets-out-plan-to-end-waiting-list-backlogs-through-millions-more-appointments",{"complexity":101,"complexityNote":102,"dataPrerequisites":103,"integrations":109},"medium","The conversation is the easy part. The work is in the scheduling rules (which visit types a patient may book, which need a referral or triage first), a reliable read and write integration with the electronic health record, and identity checks strong enough for health data. Clinical triage by the agent would push this into medical device territory, so keep it out of scope.",[104,105,106,107,108],"Scheduling rules per specialty, visit type and clinician, owned by the service","Live slot availability and waiting list data from the scheduling system","Contact details, preferred language and channel consent per patient","Historic attendance data to train or calibrate a missed appointment model","Approved preparation instructions per procedure",[110,111,112,113,114],"Electronic health record or patient administration system (scheduling and waiting list)","Patient portal or app (identity and messaging)","Telephony and messaging (voice, SMS, WhatsApp)","Referral management","Contact centre for handover to patient access staff",{"steps":116,"guardrails":132,"humanInTheLoop":138,"kpisToInstrument":139,"failureModes":145},[117,120,123,126,129],{"title":118,"detail":119},"Start with rebooking and cancellations","Cancelling and moving existing appointments is lower risk than first bookings and frees slots immediately. Add new bookings per visit type once the rules for that type are written down.",{"title":121,"detail":122},"Write the scheduling rules before the prompts","For each visit type, record who may book it, what must happen first and what the patient must be told. The agent calls these rules as tools; it never infers eligibility from the conversation.",{"title":124,"detail":125},"Make reminders two way and well timed","Test reminder timing per clinic. University Hospitals Coventry and Warwickshire found that a reminder 14 days ahead with a second one four days ahead let patients cancel early enough to rebook. Let the patient act in the same thread.",{"title":127,"detail":128},"Use risk scores to offer help, not to penalise","Use the missed appointment score to send extra reminders, offer better times or transport support. Do not use it to deny or downgrade bookings, and check it for bias by deprivation, ethnicity and age.",{"title":130,"detail":131},"Close the loop on freed slots","Connect cancellations to the waiting list so a freed slot is offered at once to suitable patients, and measure how many freed slots are actually used.",[133,134,135,136,137],"No clinical advice or triage decisions by the agent; symptoms go to staff or urgent care guidance","Only slots and visit types the scheduling system confirms for that patient","Identity verification before any appointment detail is disclosed or changed","Missed appointment scores used only to offer support, reviewed for bias","Clear AI disclosure and an easy route to a person on every channel","Patient access staff own exceptions: urgent symptoms, complex bookings across several services, safeguarding concerns and patients who ask for a person. Service managers approve the scheduling rules per visit type, and a clinical safety officer signs off the scope and reviews a sample of conversations each month.",[140,141,142,143,144],"Missed appointment rate per clinic, against a control group or the prior period","Share of freed slots rebooked from the waiting list","Booking, rebooking and cancellation containment per visit type","Call wait and abandonment rates for patient access lines","Missed appointment rates by deprivation band, ethnicity and age",[146,149,152,155],{"title":147,"detail":148},"Booking the wrong visit type","The patient arrives without the required test or preparation. Enforce rules per visit type through tools and confirm preparation in writing.",{"title":150,"detail":151},"Reminder fatigue","Too many messages lead patients to ignore them or cancel at the last minute. Test timing and frequency per clinic.",{"title":153,"detail":154},"A risk model that widens inequality","Scores that correlate with deprivation lead to overbooking or deprioritising the same groups. Use scores for support only and monitor outcomes by group.",{"title":156,"detail":157},"Symptoms missed in a booking call","A patient mentions a red flag symptom while rebooking. Detect it and route to clinical staff or urgent care guidance at once.",{"euAiAct":159,"regulations":162,"guidance":168,"controls":181,"incidents":187},{"tier":160,"basis":161},"context-dependent","Booking, rescheduling and reminders carry transparency duties: patients must be told they are dealing with AI (Article 50(1)). It becomes high risk if a public authority, or a provider acting on its behalf, uses it to evaluate eligibility for healthcare services (Annex III point 5(a)), or if it acts as an emergency healthcare patient triage system (Annex III point 5(d)). Clinical triage may also make it a medical device, which is high risk under Article 6(1) when the device needs a notified body assessment. Keep the agent to scheduling and use risk scores only to offer support.",[163,164,165,166,167],"eu-ai-act","gdpr","uk-gdpr","hipaa","us-tcpa",[169,175],{"title":170,"issuer":171,"region":172,"url":173,"note":174},"Medical devices: software and artificial intelligence (AI)","Medicines and Healthcare products Regulatory Agency","europe","https://www.gov.uk/government/publications/software-and-artificial-intelligence-ai-as-a-medical-device","UK guidance on when software, including AI, is regulated as a medical device; relevant if the agent moves from scheduling into triage.",{"title":176,"issuer":177,"region":178,"url":179,"note":180},"Declaratory ruling on AI generated voices under the TCPA (FCC 24-17)","Federal Communications Commission","north-america","https://docs.fcc.gov/public/attachments/FCC-24-17A1.pdf","AI generated voices count as artificial or prerecorded voice under the TCPA, which matters for automated reminder calls in the US.",[182,183,184,185,186],"Clinical safety case with the agent's scope written down and signed off","Audit trail of every booking, change and cancellation the agent made","Data protection impact assessment covering health data, recordings and risk scores","Regular bias review of the missed appointment model","Regression tests for scheduling rules on every change",[],{"howToBuild":189},"On Blits.ai this is an **AI agent** with **custom functions** that read slots and write bookings,\ncancellations and waiting list offers in the scheduling system over REST, so the scheduling rules\nlive in tools the agent must call. A **flow** handles identity checks and the fixed parts of a\nbooking with **show options** for slots, while the agent handles free requests such as \"can I move\nmy Tuesday appointment to an evening\". Reminder runs and slot backfill are **agentic workflows**\ntriggered on a schedule or by an API call from the hospital systems, with **human in the loop**\napproval where a service wants staff to confirm.\n\nThe same agent runs on **voice**, **SMS**, **WhatsApp**, **web chat** and, through the **API\nchannel**, in the provider's own app, in the patient's language. **Guardrails** block clinical\nadvice, **PII masking** at the gateway masks identifiers such as phone numbers and, with custom\npatterns, patient numbers, and **human handover** passes symptoms and complex cases to\npatient access staff. **Test suites** replay booking conversations for every visit type on each\nchange, and **EU and UAE data residency** keeps patient data in region.",[191,194,197,200],{"question":192,"answer":193},"Does AI actually reduce missed hospital appointments?","Public pilots suggest it can. NHS England reports a 30% fall in non attendance at Mid and South Essex NHS Foundation Trust with software that predicts missed appointments and books backup slots, and a drop from 10% to 4% in one patient group at University Hospitals Coventry and Warwickshire after AI analysis changed reminder timing. These are pilot results, and none of them is reported against a control group.",{"question":195,"answer":196},"How is this different from a general appointment booking chatbot?","A general booking agent finds a location and a free time. A patient scheduling agent has to respect referral, triage and visit type rules, write into the health record, handle preparation instructions and stay out of clinical advice, which makes the integration and the safety case the main work.",{"question":198,"answer":199},"Can a voice agent handle patient calls at scale?","WellSpan Health reports that its AI agent Ana manages more than 160,000 patient calls a month, including inbound calls and primary care scheduling. PolyAI reports 30% containment and a 72% shorter handle time for routine requests at Howard Brown Health, where the agent guides patients through scheduling in MyChart; booking, rescheduling and cancelling through Epic is the announced next phase.",{"question":201,"answer":202},"Is a missed appointment prediction model high risk?","Used to send extra reminders or offer help, it is usually not. It needs more care when scores decide who is overbooked or deprioritised, because that can limit access to care for the groups who already miss most appointments.",[204,205,206,207],"branch-and-appointment-booking-agent","outbound-reminder-and-confirmation-agent","health-prior-authorization-and-claims-adjudication","first-line-contact-centre-agent","2026-09-27",[210],{"date":208,"note":211},"First published","patient-appointment-scheduling-and-reminders-agent",[214,243,263,297,314,341],{"title":215,"useCases":216,"organization":217,"vendors":221,"summary":225,"stage":226,"year":42,"channels":227,"languages":229,"metrics":231,"outcomeDisclosed":232,"sources":233,"verification":238,"grade":240,"id":241,"organizationSlug":242},"Mid and South Essex NHS Foundation Trust: AI that predicts missed appointments and books backup slots",[212],{"name":218,"anonymized":219,"country":220,"region":172,"industry":18},"Mid and South Essex NHS Foundation Trust",false,"GB",[222],{"name":223,"role":224},"Deep Medical","platform","Mid and South Essex NHS Foundation Trust piloted software from Deep Medical that predicts which outpatient appointments are likely to be missed, using anonymised data and external factors such as weather, traffic and jobs. It offers patients more convenient times (for example evening and weekend slots for people who cannot take time off) and places intelligent backup bookings so clinical time is not lost. NHS England reports that the six month pilot cut non attendance by 30%, prevented 377 missed appointments and let an additional 1,910 patients be seen, and it announced a rollout to ten more trusts. The outcome is a reduction in missed appointments, which has no matching KPI in the taxonomy, so it is recorded in this summary rather than as a metric.","pilot",[228],"api",[230],"en",[],true,[234],{"url":41,"title":40,"publisher":235,"date":236,"archivedUrl":237},"NHS England","2024-03-14","https://web.archive.org/web/20250125233954/https://www.england.nhs.uk/2024/03/nhs-ai-expansion-to-help-tackle-missed-appointments-and-improve-waiting-times/",{"level":239,"checkedAt":208},"source-verified","B","mid-and-south-essex-missed-appointment-prediction",null,{"title":244,"useCases":245,"organization":246,"vendors":248,"summary":254,"stage":226,"year":42,"channels":255,"languages":257,"metrics":258,"outcomeDisclosed":232,"sources":259,"verification":261,"grade":240,"id":262,"organizationSlug":242},"University Hospitals Coventry and Warwickshire: AI process mining to time appointment reminders",[212,205],{"name":247,"anonymized":219,"country":220,"region":172,"industry":18},"University Hospitals Coventry and Warwickshire NHS Trust",[249,252],{"name":250,"role":251},"IBM","integrator",{"name":253,"role":224},"Celonis","University Hospitals Coventry and Warwickshire used AI based process mining with IBM and Celonis to study missed appointments, which were more common among patients with high deprivation scores. It found a spike in last minute cancellations after two SMS reminders and moved to a reminder 14 days before the appointment with a second one four days before, so patients could cancel early and the slot could be rebooked. NHS England reports that missed appointments in this subset of patients fell from 10% to 4%. The AI analysed the process; the reminders themselves are standard text messages.",[29,256],"internal-tools",[230],[],[260],{"url":41,"title":40,"publisher":235,"date":236,"archivedUrl":237},{"level":239,"checkedAt":208},"uhcw-process-mining-appointment-reminders",{"title":264,"useCases":265,"organization":266,"vendors":269,"summary":272,"stage":273,"year":42,"channels":274,"languages":275,"metrics":277,"outcomeDisclosed":232,"sources":286,"verification":295,"grade":240,"id":296,"organizationSlug":242},"WellSpan Health: Ana, a generative AI voice agent for patient calls, scheduling and outreach",[212,205],{"name":267,"anonymized":219,"country":268,"region":178,"industry":18},"WellSpan Health","US",[270],{"name":271,"role":224},"Hippocratic AI","WellSpan Health started with a Hippocratic AI voice agent that phones patients to close gaps in colorectal cancer screening, in English and Spanish, and supports low risk patients before and after a scheduled colonoscopy, with transcripts sent to clinicians and live transfer to a human where needed. By 2026 the agent, Ana, answered inbound calls and scheduled primary care appointments, and WellSpan announced an expanded partnership whose first new workflows call patients who missed imaging appointments. WellSpan reports that Ana manages more than 160,000 patient calls a month.","scaled",[28,30],[230,276],"es",[278],{"kpi":56,"value":279,"unit":280,"qualifier":281,"period":282,"claimant":283,"quote":284,"sourceUrl":285},160000,"count","at-least","per month, patient calls","organization","Ana, WellSpan’s generative AI agent developed in partnership with Hippocratic AI, currently manages more than 160,000 patient calls per month, engaging in more than 7,000 hours of conversation.","https://www.wellspan.org/articles/2026/07/30/13/05/web---hippocractic-ai-partnership-expansion",[287,290],{"url":285,"title":288,"publisher":267,"date":289},"WellSpan expands Hippocratic AI partnership to enhance operations and patient experience","2026-07-30",{"url":291,"title":292,"publisher":293,"date":294},"https://www.globenewswire.com/en/news-release/2024/09/26/2953949/0/en/WellSpan-One-of-the-First-Major-Health-Systems-in-the-World-to-Launch-Hippocratic-AI-s-Generative-AI-Healthcare-Agent.html","WellSpan One of the First Major Health Systems in the World to Launch Hippocratic AI's Generative AI Healthcare Agent","WellSpan Health (GlobeNewswire)","2024-09-26",{"level":239,"checkedAt":208},"wellspan-hippocratic-ai-patient-voice-agent",{"title":298,"useCases":299,"organization":300,"vendors":302,"summary":305,"stage":226,"year":306,"channels":307,"languages":308,"metrics":309,"outcomeDisclosed":232,"sources":310,"verification":312,"grade":240,"id":313,"organizationSlug":242},"Sheffield Children's NHS Foundation Trust: AI predictor that targets extra reminders and transport support",[212,205],{"name":301,"anonymized":219,"country":220,"region":172,"industry":18},"Sheffield Children's NHS Foundation Trust",[303],{"name":304,"role":224},"Alder Hey Innovation","Sheffield Children's piloted an AI Predictor developed by Alder Hey Innovation that estimates which children are likely to miss (\"was not brought\") an appointment, using markers that include health inequalities. Families with a predicted risk of 50% or more received an extra text reminder with an offer of support the day before; families at 85% or more were contacted and offered funded transport or a rebooking. NHS England reports that 53,800 texts were sent in the first 12 months, that recorded non attendance came in well below the expected benchmark (almost 200 more attended appointments a month), and that in a 13 week period 152 families had transport arranged and 129 appointments were rebooked.",2023,[29],[230],[],[311],{"url":41,"title":40,"publisher":235,"date":236,"archivedUrl":237},{"level":239,"checkedAt":208},"sheffield-childrens-ai-attendance-predictor",{"title":315,"useCases":316,"organization":317,"vendors":319,"summary":322,"stage":323,"year":47,"channels":324,"languages":325,"metrics":326,"outcomeDisclosed":232,"sources":335,"verification":338,"grade":339,"id":340,"organizationSlug":242},"Audibel: voice agent that captures and routes appointment calls for 400 hearing clinics",[212],{"name":318,"anonymized":219,"country":268,"region":178,"industry":18},"Audibel",[320],{"name":321,"role":224},"PolyAI","Audibel, a network of more than 400 hearing care clinics in the United States, put a PolyAI voice agent in front of its call centre, which received about 2,000 calls a day with 10 to 15 minute holds. The agent collects the caller's name, phone number and zip code, identifies the intent (such as scheduling an appointment), filters spam and hands over to a human with context. PolyAI reports 87% shorter wait times, abandonment down from 46% to 2% and appointment volume up 2% year on year. Automated booking and confirmations are named as the next phase, not yet live.","production",[28],[230],[327],{"kpi":58,"value":328,"unit":329,"qualifier":330,"period":331,"claimant":332,"quote":333,"sourceUrl":334},87,"percent","exact","call wait time","vendor","With PolyAI, Audibel cut wait times by 87% and abandonment by 44%, while spam dropped 88%.","https://poly.ai/customers/audibel",[336],{"url":334,"title":337,"publisher":321},"How Audibel reduced abandonment rates by 44% with PolyAI",{"level":239,"checkedAt":208},"C","audibel-voice-agent-appointment-calls",{"title":342,"useCases":343,"organization":344,"vendors":346,"summary":348,"stage":323,"year":42,"channels":349,"languages":350,"metrics":351,"outcomeDisclosed":232,"sources":363,"verification":366,"grade":339,"id":367,"organizationSlug":242},"Howard Brown Health: 24/7 multilingual voice agent that guides patients through scheduling in MyChart",[212],{"name":345,"anonymized":219,"country":268,"region":178,"industry":18},"Howard Brown Health",[347],{"name":321,"role":224},"Howard Brown Health, a federally qualified health center in Chicago, deployed a PolyAI voice agent named Alex that answers patient calls around the clock in several languages. Integrated with MyChart, it guides patients through scheduling appointments, test results and prescription refills, and escalates distressed callers to a person immediately. PolyAI reports 30% call containment against a 20% target, a 72% shorter average handle time for routine requests and a 4% increase in patient satisfaction. An Epic integration that lets patients create, reschedule and cancel appointments through the agent is described as the next phase.",[28],[],[352,356,360],{"kpi":55,"value":353,"unit":329,"qualifier":330,"claimant":332,"quote":354,"sourceUrl":355},30,"Initially aiming for a 20% call containment rate, PolyAI exceeded expectations by achieving 30%.","https://poly.ai/customers/howardbrownhealth",{"kpi":57,"value":357,"unit":329,"qualifier":330,"period":358,"claimant":332,"quote":359,"sourceUrl":355},72,"routine requests","72% decrease in Average Handle Time for routine requests",{"kpi":59,"value":361,"unit":329,"qualifier":330,"claimant":332,"quote":362,"sourceUrl":355},4,"Patient satisfaction scores also saw an increase of 4%, primarily driven by the enhanced ease and efficiency with which patients could schedule appointments and access services.",[364],{"url":355,"title":365,"publisher":321},"How Howard Brown Health provides personalized patient experiences with PolyAI",{"level":239,"checkedAt":208},"howard-brown-health-patient-voice-agent",1,[370,376,381,386,391],{"kpi":55,"label":371,"unit":329,"aggregate":232,"higherIsBetter":232,"n":368,"nUpTo":372,"median":353,"min":353,"max":353,"byClaimant":373,"vendorOnly":232,"points":374},"Containment rate",0,{"organization":372,"vendor":368,"regulator":372,"independent":372},[375],{"evidenceId":367,"organization":345,"value":353,"qualifier":330,"claimant":332,"grade":339,"pooled":232},{"kpi":57,"label":377,"unit":329,"aggregate":232,"higherIsBetter":232,"n":368,"nUpTo":372,"median":357,"min":357,"max":357,"byClaimant":378,"vendorOnly":232,"points":379},"Handling time reduction",{"organization":372,"vendor":368,"regulator":372,"independent":372},[380],{"evidenceId":367,"organization":345,"value":357,"qualifier":330,"claimant":332,"grade":339,"pooled":232},{"kpi":56,"label":382,"unit":280,"aggregate":219,"higherIsBetter":232,"n":368,"nUpTo":372,"median":279,"min":279,"max":279,"byClaimant":383,"vendorOnly":219,"points":384},"Interactions handled",{"organization":368,"vendor":372,"regulator":372,"independent":372},[385],{"evidenceId":296,"organization":267,"value":279,"qualifier":281,"claimant":283,"grade":240,"pooled":232},{"kpi":58,"label":387,"unit":329,"aggregate":232,"higherIsBetter":232,"n":368,"nUpTo":372,"median":328,"min":328,"max":328,"byClaimant":388,"vendorOnly":232,"points":389},"Response time reduction",{"organization":372,"vendor":368,"regulator":372,"independent":372},[390],{"evidenceId":340,"organization":318,"value":328,"qualifier":330,"claimant":332,"grade":339,"pooled":232},{"kpi":59,"label":392,"unit":329,"aggregate":232,"higherIsBetter":232,"n":368,"nUpTo":372,"median":361,"min":361,"max":361,"byClaimant":393,"vendorOnly":232,"points":394},"Satisfaction uplift",{"organization":372,"vendor":368,"regulator":372,"independent":372},[395],{"evidenceId":367,"organization":345,"value":361,"qualifier":330,"claimant":332,"grade":339,"pooled":232},{"low":397,"high":398},432000,2448000,[400,419,431,458],{"slug":204,"title":401,"shortTitle":402,"definition":403,"status":9,"industries":404,"functions":409,"patterns":411,"audience":33,"autonomy":413,"adoptionStage":35,"evidenceCount":361,"publicEvidenceCount":361,"organizations":414,"bestGrade":240,"headline":242,"lastVerified":208,"indexable":232},"AI agent for branch finding and appointment booking","Branch and appointment booking","A conversational agent that finds the nearest suitable location, checks opening hours and which services it offers, books an in person or video appointment with the right specialist, and records the reason for the visit so staff are prepared. In banking it answers \"where is my nearest branch\" and books the mortgage or business banker; the same job exists in retail, healthcare and property.",[405,406,407,18,408],"cross-industry","banking","retail-and-ecommerce","real-estate",[20,410],"sales",[24,26,412,23],"rag-knowledge-assistant","autonomous",[415,416,417,418],"Bank of America","Best Buy","Hemominas","MOGUL.sg",{"slug":205,"title":420,"shortTitle":421,"definition":422,"status":9,"industries":423,"functions":425,"patterns":426,"audience":33,"autonomy":34,"adoptionStage":35,"evidenceCount":427,"publicEvidenceCount":361,"organizations":428,"bestGrade":240,"headline":242,"lastVerified":430,"indexable":232},"AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent that contacts customers about something they already booked or ordered (an appointment, a delivery, a reservation or a service visit) to remind them, confirm attendance and let them cancel or move it in the same conversation, by phone, SMS, WhatsApp or email. It is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion.",[405,18,424],"government",[20,21],[23,24,26,25],5,[301,247,429,267],"U.S. Department of Veterans Affairs","2026-09-26",{"slug":206,"title":432,"shortTitle":433,"definition":434,"status":9,"industries":435,"functions":437,"patterns":440,"audience":445,"autonomy":446,"adoptionStage":35,"segment":438,"evidenceCount":427,"publicEvidenceCount":427,"organizations":447,"bestGrade":240,"headline":453,"lastVerified":208,"indexable":232},"AI for health insurance prior authorization and claims adjudication support","Health prior authorization and adjudication","AI that reads prior authorization requests, medical claims and appeals with their clinical and billing documents, extracts diagnoses, treatments and costs, checks them against the policy and published clinical criteria, and prepares a summary and recommendation for a clinician or adjudicator, who makes every adverse decision.",[436,18],"insurance",[438,439,21],"claims","case-management",[441,442,412,443,444],"document-processing","summarization","classification-and-routing","content-generation","employee-facing","copilot",[448,449,450,451,452],"Acentra Health","AdvanceCare","Centers for Medicare and Medicaid Services","ICICI Lombard","Manulife",{"kpi":57,"label":377,"unit":329,"n":454,"nUpTo":372,"kind":455,"value":456,"qualifier":457,"claimant":332,"organization":448,"vendorReported":232},2,"reported",50,"approximately",{"slug":207,"title":459,"shortTitle":460,"definition":461,"status":9,"industries":462,"functions":467,"patterns":468,"audience":33,"autonomy":34,"adoptionStage":469,"segment":470,"evidenceCount":471,"publicEvidenceCount":472,"organizations":473,"bestGrade":240,"headline":490,"lastVerified":208,"indexable":232},"AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[405,406,463,464,465,407,466],"payments","telecommunications","travel-and-hospitality","wealth-and-asset-management",[20],[24,23,412,443],"mainstream","front-office",25,18,[474,475,415,476,477,478,479,480,481,482,483,484,485,486,487,488,489],"Air India","Airbnb","Bank of the Philippine Islands","BT Group","Commonwealth Bank of Australia","Ingka Group","JetBlue","Klarna","Lufthansa Group","Mobily","NatWest Group","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":55,"label":371,"unit":329,"n":491,"nUpTo":372,"kind":492,"value":493,"qualifier":330,"claimant":242,"organization":242,"vendorReported":219},7,"median",47,{"indexable":232,"reasons":495},[],[497,503,508,516,523,529,535,541,549,555,562,568,575,582,588,593,600,606,611,617,623,627,633,638,643,650,657,662,668,675,681,687,694,699],{"id":163,"label":498,"issuer":499,"region":172,"url":500,"description":501,"useCases":502,"indexable":232},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":164,"label":504,"issuer":499,"region":172,"url":505,"description":506,"useCases":507,"indexable":232},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":509,"label":510,"issuer":511,"region":512,"url":513,"description":514,"useCases":515,"indexable":232},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":517,"label":518,"issuer":519,"region":178,"url":520,"description":521,"useCases":522,"indexable":232},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":524,"label":525,"issuer":499,"region":172,"url":526,"description":527,"useCases":528,"indexable":232},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":165,"label":530,"issuer":531,"region":172,"url":532,"description":533,"useCases":534,"indexable":232},"UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":536,"label":537,"issuer":538,"region":172,"url":539,"description":540,"useCases":493,"indexable":232},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",{"id":542,"label":543,"issuer":544,"region":545,"url":546,"description":547,"useCases":548,"indexable":232},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":550,"label":551,"issuer":552,"region":545,"url":553,"description":554,"useCases":471,"indexable":232},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",{"id":556,"label":557,"issuer":558,"region":512,"url":559,"description":560,"useCases":561,"indexable":232},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":563,"label":564,"issuer":565,"region":178,"url":566,"description":567,"useCases":561,"indexable":232},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":569,"label":570,"issuer":571,"region":172,"url":572,"description":573,"useCases":574,"indexable":232},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":576,"label":577,"issuer":578,"region":512,"url":579,"description":580,"useCases":581,"indexable":232},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":583,"label":584,"issuer":499,"region":172,"url":585,"description":586,"useCases":587,"indexable":232},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":589,"label":590,"issuer":499,"region":172,"url":591,"description":592,"useCases":587,"indexable":232},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":594,"label":595,"issuer":596,"region":178,"url":597,"description":598,"useCases":599,"indexable":232},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":601,"label":602,"issuer":499,"region":172,"url":603,"description":604,"useCases":605,"indexable":232},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":166,"label":607,"issuer":608,"region":178,"url":609,"description":610,"useCases":605,"indexable":232},"HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":612,"label":613,"issuer":614,"region":512,"url":615,"description":616,"useCases":605,"indexable":232},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":618,"label":619,"issuer":499,"region":172,"url":620,"description":621,"useCases":622,"indexable":232},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":167,"label":624,"issuer":177,"region":178,"url":625,"description":626,"useCases":622,"indexable":232},"Telephone Consumer Protection Act","https://www.fcc.gov/consumers/guides/stop-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":628,"label":629,"issuer":544,"region":545,"url":630,"description":631,"useCases":632,"indexable":232},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":634,"label":635,"issuer":499,"region":172,"url":636,"description":637,"useCases":632,"indexable":232},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":639,"label":640,"issuer":499,"region":172,"url":641,"description":642,"useCases":632,"indexable":232},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":644,"label":645,"issuer":646,"region":172,"url":647,"description":648,"useCases":649,"indexable":232},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":651,"label":652,"issuer":653,"region":178,"url":654,"description":655,"useCases":656,"indexable":232},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":658,"label":659,"issuer":499,"region":172,"url":660,"description":661,"useCases":656,"indexable":232},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":663,"label":664,"issuer":499,"region":172,"url":665,"description":666,"useCases":667,"indexable":232},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",6,{"id":669,"label":670,"issuer":671,"region":672,"url":673,"description":674,"useCases":427,"indexable":232},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":676,"label":677,"issuer":678,"region":172,"url":679,"description":680,"useCases":361,"indexable":232},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":682,"label":683,"issuer":684,"region":172,"url":685,"description":686,"useCases":361,"indexable":232},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":688,"label":689,"issuer":690,"region":545,"url":691,"description":692,"useCases":693,"indexable":232},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":695,"label":696,"issuer":499,"region":172,"url":697,"description":698,"useCases":693,"indexable":232},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":700,"label":701,"issuer":702,"region":178,"url":703,"description":704,"useCases":693,"indexable":232},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598295703]