Dementia care is a matching problem

People living with dementia, their families, the professionals who care for them, and the companies that build products for them all want something different. This page collects what I found about where those wants meet, where they collide, and what it would take for AI to deliver the right service to each person.

Fit People living with dementia Families Care professionals Companies want company, things to do, information, dignity want safety, fear the burden need accurate, workable signals sell detection, monitoring, devices Unmet needs rated 0.95 by the person, 1.66 by the family GPS behind only 139 of 16,729 people found Alert model: 71% sensitivity, 75% specificity Up to 80% unaware of their own deficits the right service for this person, at this stage
evidence of mismatchwhat a personalized service has to reconcile
  • Person and family: unmet needs rated 0.95 by the person, 1.66 by the family
  • Families and companies: GPS behind only 139 of 16,729 people found
  • Companies and professionals: alert model at 71% sensitivity, 75% specificity
  • Professionals and person: up to 80% unaware of their own deficits
Each dashed edge carries one finding from the research below. Sources are in sections 3 to 6.

1We are thinking about people with dementia

Our team at the Tokyo Forum Youth Session is studying how AI affects people with disabilities, and we chose dementia as our focus. Japan is the clearest place to see why: the numbers are large, the treatment window is narrow, and much of the risk is modifiable.

The scale in Japan

A Ministry of Health, Labour and Welfare (MHLW) study led by Professor Toshiharu Ninomiya (Kyushu University) estimated 4.432 million older adults with dementia in 2022 (12.3% of people aged 65+) and 5.585 million with mild cognitive impairment, MCI (15.5%). Together that is about 28% of older adults. The estimate projects 5.842 million with dementia and 6.128 million with MCI by 2040. Professor Ninomiya notes that MCI caught early can partly return to normal through lifestyle change, which is why prevention matters.

Older adults with dementia and MCI in Japan, millions. Dementia: 4.432 (2022, 12.3%), 4.716 (2025, 12.9%), 5.231 (2030, 14.2%), 5.842 (2040, 14.9%), 5.866 (2050, 15.1%), 6.451 (2060, 17.7%). MCI: 5.585 (15.5%), 5.643 (15.4%), 5.931 (16.0%), 6.128 (15.6%), 6.312 (16.2%), 6.322 (17.4%). Source: MHLW, 2024.

The treatment window

The two antibody drugs approved in Japan, lecanemab (Leqembi, on sale from December 2023) and donanemab (Kisunla, from November 2024), are indicated only for slowing progression in MCI due to Alzheimer's disease and mild dementia. In July 2026 APOE genetic testing was added to insurance coverage, and in August 2026 PMDA revised both drugs' warnings to recommend considering the test before treatment. The stage that families tend to miss is exactly the stage where treatment is possible.

Much of the risk is modifiable

The 2024 Lancet Commission estimates that about 45% of dementia cases could be prevented or delayed by addressing 14 risk factors across life: less education, hearing loss, high LDL cholesterol, depression, traumatic brain injury, physical inactivity, diabetes, smoking, hypertension, obesity, excessive alcohol, social isolation, air pollution and untreated vision loss. Hearing loss accounts for about 7%, LDL cholesterol about 7%, and vision loss about 2%.

Safety: people who go missing

Japanese police received missing-person reports for 17,345 people with dementia in 2025, about one in five of all 80,486 missing-person reports. The record was 19,039 in 2023. Of the 16,729 cases resolved in 2025, GPS devices were involved in only 139, and 118 of those people (84.9%) were found on the day the report was filed. Once 15 or more days pass, confirmed deaths outnumber survivors. In 2024, 491 people died, and 382 of them (77.8%) were found within 5 km of where they went missing.

Missing-person reports for people with dementia in Japan. 2015: 12,208. 2021: 17,636. 2022: about 18,700. 2023: 19,039 (record). 2024: 18,121. 2025: 17,345 (80s and older 10,661; 70s 5,752; most by prefecture Kanagawa 1,898, Osaka 1,857, Saitama 1,780; Tokyo 1,005). Source: National Police Agency, via Kyodo, ANN, Jiji and CareNet.

2Four stakeholders, four agendas

When I think about dementia, the most important thing is the match and mismatch between what each group wants.

A product can be clever and still fail if it serves one group at the expense of another. These are the four groups I keep in view.

StakeholderWhat they wantWhat they fear or struggle with
People living with dementiaCompany, things to do in the day, information about care, help with distress, going out on their own, staying in their communityBeing watched, being labelled, losing independence, burdening their family
FamiliesSafety, knowing where the person is, support with memory problems, less physical and emotional strainNot noticing decline, the person going missing, their own exhaustion
Care professionals
care managers, clinicians, care workers, navigators
Accurate signals, the right local services to recommend, fewer preventable hospital admissionsInformation overload, false alarms, time pressure
Companies
device makers, insurers, pharma, startups
Products that scale and pay: detection, monitoring, diagnostics, insurance add-ons, early identification for treatmentLow adoption, regulation of diagnostic claims, unclear payers

So I researched whether their needs actually match. Sections 3 to 6 are the evidence; section 7 sums it up.

3What people with dementia say they need

The most consistent finding is a perception gap. People with dementia report fewer needs than their family carers report for them, and the needs they do name are about company and meaning, not safety.

They report about half the needs their carers report

In the European Actifcare study of people with middle-stage dementia living at home, the person rated an average of 0.95 unmet needs, the family carer rated 1.66 for them, and a trained researcher rated 1.85. For met needs, the person rated 5.5 and the carer 8. Carers reported almost twice as many needs, in line with earlier research.

Mean unmet needs per person, by who is rating. Person 0.95, family carer 1.66, researcher 1.85. Source: Actifcare, Aging and Mental Health, 2017.
Share with an unmet need, by domain (%). Company: person 15, carer 24, researcher 28. Information: 13, 10, 13. Daytime activities: 9, 28, 29. Researchers also rated psychological distress 14% and benefits 10%.

A study by Miranda-Castillo and colleagues (2013) found the same pattern: people with dementia reported fewer needs than caregivers and professionals, and the person most often named psychological distress as their top unmet need, while caregivers most often named memory (32.5%), daytime activities (16.0%) and company (13.1%). An earlier study cited in the same paper found people with dementia naming information (34.5%), company (28%) and daytime activities (25.2%), while their caregivers named daytime activities (40%), company (36.8%) and memory (36.6%).

Unmet needs matter for outcomes. In a study of 152 people with dementia living at home, the most frequent unmet needs were daytime activities (50.7%), company (39.5%) and help with psychological distress (30.9%). More behavioural symptoms, weak community networks, a younger carer and higher carer anxiety predicted more unmet needs, and people whose carers report more unmet needs are more likely to move into a care home or die.

Living alone raises every need

Most common unmet needs (%). Living alone: daytime activities 54.0, company 52.0, psychological distress 44.0, eyesight or hearing 32.0, accidental self-harm 32.0. Living with others: daytime activities 49.0, company 33.0, psychological distress 24.5, eyesight or hearing 16.7. Help with company, distress and eyesight or hearing came mostly from family and friends. Source: International Psychogeriatrics, 2024.

Part of the gap is lost awareness

Anosognosia, being unaware of one's own deficits, is estimated to affect 20% to 80% of people with Alzheimer's dementia depending on how it is measured, and up to 80% at the dementia stage in an early study. One study found it in 13.6% to 17.3% of mild cases but 44.2% to 55.8% of moderate cases; another found 39.5% of 177 people with mild or moderate Alzheimer's at baseline. A meta-analysis suggests awareness is mildly reduced at the MCI stage and severely reduced in dementia.

Anosognosia by severity (%), two measurement methods. Mild: 13.6 (ERS) and 17.3 (AQ-D). Moderate: 44.2 (ERS) and 55.8 (AQ-D). Source: Revista de Neurología, 2012.

Design implication: at the MCI or early stage most people can still judge their own needs. That is the window to ask for consent and record what they want, before awareness fades.

Going out alone: autonomy with support

In a UK study of 12 people with dementia, 3 caregivers and 5 older people, participants said a GPS device would help them go out independently with more confidence. They were less worried about the ethics of "tagging" than academics and the media, but wanted the device to be discreet so it would not add stigma. In a Swedish study, everyone with dementia did daily outdoor activities, mostly alone. UK users also rejected devices that beep or flash, imagining an alarm going off in a supermarket.

4How much autonomy, and where people live

Japan measures autonomy directly. The "independence in daily living of older adults with dementia" scale (認知症高齢者の日常生活自立度) is assigned during long-term care certification.

RankMeaningExamples given by MHLW
ISome dementia, but largely independent at home and in society
IIaSymptoms disrupt daily life outside the home, but independent if someone keeps an eye on themOften getting lost; noticeable mistakes in shopping, paperwork or money management
IIbThe same, also inside the homeCannot manage medication; cannot stay home alone to handle phone calls or visitors
IIIa / IIIbSymptoms appear and care is needed, mainly in the daytime (a) or at night (b)
IVSymptoms appear frequently and constant care is needed
MMarked psychiatric symptoms, problem behaviour or serious physical illness needing specialist careDelirium, delusions, agitation, self-harm or harm to others

National picture

In 2010, 2.80 million older adults were at rank II or above (9.5% of people 65+), about 60% of everyone with a care certification. Including rank I and people never certified, the estimated dementia total was about 4.39 million, plus about 3.8 million with MCI. The count excludes people who never applied for certification, who are often early-stage: exactly the people a wearable for early detection would serve.

Older adults at rank II or above, millions. 2.80 (2010), 3.05 (2012), 3.45 (2015, projected), 4.10 (2020, projected), 4.70 (2025, projected, 12.8% of 65+). Source: MHLW, 2012 estimate.
Autonomy rank among all 4.33 million care-certified people, by setting (%, approximate). My reading of an MHLW chart (2018). Overall: independent 21, I 19, IIa 11, IIb 17, IIIa 16, IIIb 6, IV 9, M 2. At home (3.34 million): 26, 22, 12, 17, 12, 4, 5, 1. Residential (0.34 million): 5, 9, 10, 22, 26, 10, 15, 3. Facilities (0.85 million): 3, 7, 8, 16, 29, 12, 22, 4.

Tokyo: the share living at home falls as autonomy falls

Tokyo, FY2016, by autonomy rank. At home: no dementia 117,656 (87.2%); I 88,460 (82.6%); II 101,759 (69.5%); III 49,242 (47.0%); IV 15,997 (35.1%); M 3,336 (34.5%). Outside the home: 17,329; 18,634; 44,601; 55,520; 29,575; 6,347. Of 306,377 people at rank II or above, 55.6% lived at home. Source: Tokyo Metropolitan Government distribution survey, FY2016.

Tokyo's FY2013 survey found 66.2% of people at rank I or above and 59.8% at rank II or above living at home, with rank II or above projected to grow from about 270,000 to about 440,000 by 2025. About half of people at home with suspected dementia live alone or as a couple only. Tokyo repeats the survey every three years with municipal breakdowns; the latest uses a 1 November 2025 reference date.

Osaka City, April 2026

Care-certified people 65+ in Osaka City, 1 April 2026. Rank II or above: 60,546 at home and 42,322 in facilities (102,868 total, about 59% at home). By age: 65 to 74, 6,044 at home and 3,302 in facilities; 75+, 54,502 and 39,020. Independent or rank I: 82,930 at home and 14,846 in facilities. Source: Osaka City.

Korea, and gaps in the other regions

Korea, severity distribution (%). 2012 national survey: very mild 17.4, mild 41.4, moderate 25.7, severe 15.5 (541,000 people, prevalence 9.18%). 2025 estimate from the 2023 survey: mild 67.7, moderate 29.5, severe 2.8. Categories changed between the two, so they are not directly comparable.
Korea, annual cost per person by severity, 2015 (million KRW). Very mild 14.84, mild 17.40, moderate 25.71, severe 31.87. Source: Central Dementia Center, 2017.

Korea estimated 954,789 people aged 60+ with dementia in 2024 (6.76%), publishes estimates down to district level on data.go.kr, and opened 256 dementia relief centers (치매안심센터) in every district by December 2019. Hong Kong has a high institutionalization rate compared with other developed economies, partly because community care capacity is limited. I did not find comparable public breakdowns for mainland China or the Philippines; teammates from those regions are better placed to find local sources.

5What families and the public fear

In the Cabinet Office survey published in 2025 (1,551 responses from 3,000 people aged 18+), the top worry about getting dementia oneself was burdening one's family physically or mentally (74.9%). When a family member gets dementia, the top worry was that they would cause trouble for people around them (46.5%). In 2025, 60.5% of respondents had met someone with dementia.

"What is your image of dementia?" (%), Cabinet Office. 2015: can live independently with own adjustments 6.8; can live in the community with support 33.5; needs a care facility 35.9; causes trouble so cannot stay in the community 7.6; becomes unable to do anything 10.9; other or don't know 5.3. 2019: 6.9; 32.6; 40.0; 8.0; 8.4 (derived from the reported pessimistic total of 56.4%); other 4.1.

Shimane Prefecture's 2022 survey with Meiji Yasuda Life found the same: 75.4% feared burdening their family, and 61.5% wanted local government to focus on reducing family burden. In the 2023 survey, 82% of teenage respondents thought living in the community would be difficult with dementia. Japan's Dementia Basic Act took effect in January 2024 and promotes a "new view of dementia": that people can keep doing what they want and live with hope in their community after diagnosis. In a 2025 Nagasaki survey, 74.1% had never heard of that idea.

Fear of burden can work as common ground. A person who fears burdening their family may accept support that is framed as giving the family peace of mind, rather than as watching them.

6What companies and researchers are building

Most products target detection, monitoring and safety. The science behind passive sensing is getting strong.

What wearable signals can predict

Change in dementia risk linked to wearable signals (%). UK Biobank Watch Walk study, 47,371 people aged 60+ (mean 67), median 7.5 years of follow-up, 387 diagnoses (0.8%): each standard deviation faster maximal walking speed (4.6 cm/s) cut hazard by 32%, more daily steps by 30%, while more step-time variability raised it 17%. Running less and going to bed before 9 pm were also independent predictors, and the model performed comparably to established risk scores. ARIC study in Neurology (2026), about 2,200 adults averaging 79 years, about 12 days of monitoring and about 3 years of follow-up, 176 diagnoses: each standard deviation weaker rhythm raised risk 54%, the weakest-rhythm group had about 2.5 times the risk, and activity peaking at 2:15 pm or later raised risk 45% (10% vs 7% diagnosed).

Gait speed and stride regularity measured this way also track brain structure: in 6,412 UK Biobank participants they were associated with white matter lesions and the volume of frontal, hippocampal, insular and amygdala grey matter.

Phone and watch at scale

The Biogen and Apple "Intuition" study turned 126,640 app downloads into 23,004 enrolled US adults and collected up to 24 months of iPhone and Apple Watch data, including passive device use, surveys and repeated cognitive tests. It showed remote tests correlate with clinic tests, but it was stopped early when Biogen's priorities changed.

What the reviews warn

  • A 2026 JMIR systematic review of 49 studies since 2020 concludes that wearable behavioural markers show promise for early risk stratification. Some headline accuracies are from tiny samples: 93.5% with 222 people, and about 94–95% with only 33 people (5 with dementia).
  • A 2026 npj Digital Medicine review found 21 studies of 16 wearable EEG devices for MCI. MUSE 2 was the most used (7 of 21), followed by EPOC X (3 of 21). Support vector machines were used in 13 of 21 studies (61.9%). Accuracy was reported in 76.2% of studies and AUC in 38.1%, with wide variation in protocols.
  • A 2026 JMIR Aging review screened 2,824 records and kept 17 studies on sedentary behaviour in MCI and dementia; evidence on differences in sedentary time was inconsistent.
  • An earlier review of digital biomarkers from computerized memory tests found, for detecting MCI, sensitivity of 42.0–85.8%, specificity of 66.0–93.3% and AUC of 0.53–0.93.

Products and programs in 2025–2026

WhoWhatStatus and numbers
Samsung Brain HealthGalaxy Watch, Ring and phone analyse voice, gait and sleep for early signs of cognitive decline; processed mainly on-deviceShown at CES 2026; in clinical validation with institutions in Korea and elsewhere; planned beta in selected markets; release date unclear; not a medical device
AppleHypertension pattern notifications on Apple Watch; hearing check and hearing assistance on AirPods ProHypertension notifications live in Japan since 4 December 2025, using 30 days of optical sensor data, Series 9 or later and Ultra 2 or later; over 1 million people expected to be notified in the first year. AirPods hearing feature cleared by FDA after a 118-person trial; offered in Japan as "hearing assistance", not a regulated hearing aid. Tokai University registered a study on 26 August 2026 testing AirPods Pro 2/3 in older adults with mild hearing loss
FRONTEO and Shionogi (SDS-881)AI that analyses 10+ minutes of free conversation to judge whether cognitive decline is likelyPriority review designation February 2025; clinical trial from June 2025; aiming for approval in FY2026. A consumer app on the same engine has been an add-on to Nippon Life's dementia insurance since October 2025
Minder (UK Dementia Research Institute)Passive home monitoring with low-cost devices such as sleep mats and activity sensors, plus machine learning and a clinical monitoring teamUK: over 980,000 people with dementia, about 60% at home; over a quarter of NHS beds occupied by people with dementia; about a fifth of their admissions preventable. Agitation model on 32,896 person-days from 63 people: 71.3% sensitivity, 75.3% specificity; agitation affects about 30% of people with dementia
CrossSenseAI companion "Wispy" on smart glasses that recognises household objects and guides people through daily tasksWon the £1 million Longitude Prize on Dementia (March 2026). Participants named 82% of household items with the glasses vs 46% without, and 78% one hour after removing them. Public launch targeted for early 2027. The £4.4 million prize funded 24 technologies since 2022
CareYayaMedaCareLLM on smart glasses: recognises faces and objects and gives prompts through a bone-conduction earpieceDeveloping models to track disease progression from video, audio and gaze data
Japanese municipalities and security firmsGPS trackers for people at risk of going missingSee price chart below
Monthly price of GPS tracking for dementia in Japan (yen). Minato Ward program 500; Fukuoka City program 1,250; Coco-Secom (Bunkyo Ward plan) 1,320, plus 220 per operator lookup and 11,000 per rush dispatch; shoe-insert GPS (Bunkyo Ward plan) 3,300. Sanjo City subsidises up to 10,000 initial and 3,000 a month for three months; Kofu City up to 15,000 for a device and 1,000 a month.
CrossSense trial, household items named correctly (%). Without glasses 46; with glasses 82; one hour after removing them 78.

Acceptance is the bottleneck

  • In a 2025 survey, 74.4% of older adults were interested in wearable health monitoring, but 54.9% reported anxiety, along with worries about over-surveillance.
  • In interviews, older people linked smartwatches to surveillance and loss of control in their own space, and said feedback and alerts could make them doubt their own decisions.
  • A 2025 meta-synthesis of 58 studies on wearables in dementia research (only 11, or 19%, were trials) found privacy concerns in 15 studies (26%). Adherence depended on whether the person could keep remembering what the device was for, and on keeping devices simple.
  • A 2025 focus-group study of 31 adults aged 65+ found reluctance to adopt fall-detection devices often came from stigma, because the devices were seen as symbols of frailty.

7Matches and mismatches

Putting sections 3 to 6 together, this is what I found.

Mismatch

Person and family rate needs differently: 0.95 vs 1.66 unmet needs. The person names company, information, activities and distress; the family names memory and safety.

Mismatch

Companies mostly sell detection and monitoring, while people ask for company and dignity. 54.9% of older adults are anxious about monitoring, and devices that signal frailty carry stigma.

Mismatch

Families and GPS providers: municipal GPS costs as little as 500 yen a month, yet GPS was behind only 139 of 16,729 people found in 2025.

Mismatch

Companies and professionals: alert models at 71% sensitivity and 75% specificity would flood families with false alarms unless clinicians filter them, as Minder does. Diagnostic claims need regulatory approval, which takes years (FRONTEO's path).

Mismatch

Professionals and people: up to 80% of people with Alzheimer's dementia are unaware of their own deficits, so needs must be judged partly from outside.

Match

Going out alone: people see discreet GPS as a confidence tool, and families see it as safety. Same device, different framing.

Match

Burden: 74.9% fear burdening their family, and families want less burden. Both point to support that relieves the family.

Match

Early detection: drug makers need people identified at the MCI stage, wearable signals can flag risk years ahead, and people at that stage can still give consent.

Match

Prevention: hearing and blood pressure are modifiable risks, and approved consumer tools for both already exist in Japan.

8What I realized: everyone needs a service that fits

We all need personalized services for each person with dementia. AI should not only make services better; it should make them the most suitable for each person.

The evidence shows that needs differ by person, by stage and by place: living alone versus with others, rank I versus rank III, Tokyo versus Osaka versus Seoul. A one-size product gets low adoption, the way GPS does. A good match also has to answer to all four stakeholders at once.

Examples of the autonomy tension

SituationPerson tends to wantFamily tends to wantPersonalized response
Going outKeep walking freelyKnow where they areShare location only on unusual patterns or on request, not continuously
MoneyKeep shopping and payingPrevent mistakes and scamsAlerts on unusual transactions, set by the person in advance
MedicationTake their own pillsGuarantee adherenceRemind the person first; tell family only after repeated misses
Daily trendsNot be told they are failingEarly warningThe person sees their trend; family sees only what the person chose to share
Social lifeCompany and purposeSafetyPrompts for connection and activities, not just alarms

Fit changes with stage

StageWho the service is forHow it behaves
MCI or rank IThe personPrivate by default; prevention (hearing, blood pressure, walking); records the person's own wishes while awareness is intact
Rank II (about 70% still at home in Tokyo)The person, with family sharing they choseFamily sees trends and unusual events, not continuous tracking; going-out support
Rank III and aboveCarers and familySafety alerts, care coordination, decisions guided by Japan's MHLW guideline on supporting decision-making

9So first, maybe we need a marketplace

If the goal is the best fit for each person, the first step is a place where the person, their family and their professionals can see and choose among services: companionship, activities, information, going-out support, prevention, diagnosis, home care and respite. The difference from a directory is that the ranking is by fit to this person, not by who pays most for placement.

Person and family Own wishes and sharing choices Stage and autonomy rank Living alone or with others Consented data from devices Region and coverage Family sees what is shared Matching layer AI ranks services by fit Explains why each match Learns from outcomes Escalates to a human Reviewed by care professionals Services Company and activities Information and navigation Going-out support and GPS Prevention: hearing, BP, exercise Diagnosis and treatment Home care and family respite Payers: families, insurers, municipalities, public programs
A concept, not a product yet. The person's own wishes sit at the start of the chain, and professionals sit inside it.

10Ways to build it, and who is already trying

I found several ways to create this. Each example solves one part of the problem.

Counsel Health ("Counsel AI"), United States

AI front door with physicians in the loop

Founded in 2023, Counsel combines medical AI with in-house physicians, delivered through messaging. It raised an $11 million seed round led by a16z in October 2024 and a $25 million Series A from a16z and GV in October 2025. Its physician group is licensed in all 50 states; the AI uses clinical guidelines and patient records and is trained to escalate cases that need a human. a16z describes the AI as remembering context across every interaction, so personalization improves over time. Physicians use a "clinician cockpit" that brings together records, specialty guidelines and research. Its chronic-care offer starts at $29 for a consultation with follow-up. In September 2026 it joined Medicare's ACCESS model for chronic care, and it launched Counsel Studio so other organizations can embed its care in their own apps.

Lesson: personalization comes from memory of the person plus human oversight, and distribution comes through payers and partners.

Welmo (ウェルモ), Japan

Matching for care managers

Since 2014 Welmo has run ミルモネット, a local care information platform with over 30,000 listings that connects care providers with care managers. In Fukuoka's seven wards alone there were about 2,100 care providers and over 1,100 care managers, too many to know by heart. Providers update their own pages for free, including availability, because it works as a sales channel; base data comes from MHLW open data. Its AI, ミルモぷらん, helps care managers write care plans and suggests providers filtered by services, staffing and add-ons, supporting fair introduction of several providers rather than one. Welmo has raised about ¥5.6 billion.

Lesson: professionals are a key user of the marketplace, and suppliers will keep data fresh if it brings them clients.

Lottie, United Kingdom

Curated consumer marketplace, free for families

Founded in 2021, Lottie lets families find, compare and contact care homes, home care and retirement living, with regulator ratings and funding types on each listing. It raised a £16.35 million ($21 million) Series A led by Accel, listed over 4,000 services at that time, and has passed 10 million users. It earns from providers, including an AI enquiry handler called Eliza launched in April 2026, which keeps the service free for families.

Lesson: families will use a marketplace if it is free and trusted; providers can fund it.

CareYaya, United States

AI matching of people, not products

CareYaya matches families with college students studying healthcare, after background checks and video interviews; families describe their loved one's needs and are matched for one-off or ongoing care, with long-term matches prioritized. It had over 25,000 students on the platform in 2024 and was selected for the AARP AgeTech Collaborative. It also equips caregivers with AI and smart glasses to track dementia progression.

Lesson: matching the right human is part of personalization, and the carer can also collect useful data.

Medicare GUIDE model and Rippl, United States

A public payer creates the navigation market

Medicare's GUIDE model pays for dementia care coordination and management, with care navigators who connect people and caregivers to clinical and community services, caregiver training, a 24/7 support line, a monthly Dementia Care Management Payment, and up to $2,500 a year of respite care for people with moderate or severe dementia who have a caregiver. Early participants included Northwell Health, UCSF, UCLA Health, Emory and Rippl Care, and the Alzheimer's Association runs a navigation service with Rippl in Illinois, Missouri, Texas and Washington.

Lesson: when someone pays for navigation, matching becomes a business.

Insurers and public hubs, Japan and Korea

Distribution through existing trust

Nippon Life bundles the FRONTEO and Shionogi conversation app with its dementia insurance, and Shionogi is in talks with other insurers and aims to offer it to banks and driver's license centers. Japanese municipalities already budget for GPS programs, and community support centers (地域包括支援センター) are the local entry point for care. Korea's 256 dementia relief centers play a similar role.

Lesson: a marketplace for Japan could launch through insurers or municipalities rather than direct to consumers.

ExampleWhat is matchedWho paysRole of AI
Counsel HealthPerson to medical advice and physiciansMembers, payers, employers, Medicare modelRemembers the person, triages, escalates
WelmoCare manager to local providersProviders and care businesses (SaaS)Drafts care plans, suggests providers
LottieFamily to care homes and home careProvidersHandles provider enquiries; data tools
CareYayaFamily to student caregiversFamilies pay caregivers directlyMatching; progression tracking
GUIDE and RipplPerson and caregiver to services via navigatorsMedicareNot central; human navigators
Nippon Life and ShionogiPolicyholder to cognitive checkInsurerConversation analysis

None of these yet ranks services by fit to one person with dementia across all four stakeholders. That is the gap.

11How AI already combines medical data: MIT's HAIM

To match well, the marketplace needs data about the person. Hospitals already have a pattern for combining data: the Holistic AI in Medicine (HAIM) framework from MIT, published in npj Digital Medicine in 2022.

HAIM framework: patient files from a hospital are split into tabular data, time series, natural language and medical images, each turned into embeddings by its own feature extractor, concatenated, and fed to models such as XGBoost. Other sources such as social media, wearable sensors and behaviour are marked as future potential.
Fig. 1 of Soenksen et al., "Integrated multimodal artificial intelligence framework for healthcare applications", npj Digital Medicine 5, 149 (2022). Reproduced under CC BY 4.0. Open the figure on PMC, read the paper, or see the code on GitHub.

The numbers

The authors combined MIMIC-IV (de-identified records of 383,220 patients from the ICU and emergency department of Beth Israel Deaconess Medical Center in Boston, 2008–2019) with MIMIC-CXR (377,110 chest X-rays from 227,835 imaging studies). The resulting dataset had 34,537 samples from 7,279 hospital stays and 6,485 patients. They trained 14,324 models across 4 data types, 11 data sources and 12 tasks. Combining data improved results by 6–33% over single-source models. Images contributed most to chest diagnoses; time series contributed most to length-of-stay and 48-hour mortality. Each added data type helped, with diminishing returns as sources overlapped.

Minimum improvement in AUROC for each chest diagnosis (%). Fracture 6 (557 samples), lung lesion 7 (930), enlarged cardiomediastinum 9 (3,206), consolidation 10 (4,465), pneumonia 8 (7,225), lung opacity 7 (14,136), atelectasis 6 (15,213), pneumothorax 8 (17,159), edema 10 (17,182), cardiomegaly 10 (18,571).
Range of AUROC improvement over single-source models (%). Chest pathology 6–22 (10,230 models); length of stay 8–20 (2,047 models); 48-hour mortality 11–33 (2,047 models). Length-of-stay and mortality used 45,050 samples.
What the 4,845-number patient vector is made of. Demographics 6; chart events 99; lab events 242; procedure events 110; radiology notes 768; ECG notes 768; echo notes 768; image diagnosis probabilities 18; image dense features 1,024; multi-image probabilities 18; multi-image dense features 1,024.

What HAIM collects in each data type

Data typeIn general, per the paperWhat HAIM usedHow it was processed
TabularAge, demographics, procedures, history, billing codes6 demographic variablesNormalized numbers
Time seriesPulse oximetry, blood chemistry, respiratory analysis, ECGs, ultrasound, in-vitro tests, wearable sensors9 chart events, 23 lab tests, 10 procedure events (full lists in section 12)Statistics per signal: length, max, min, mean, median, SD, variance, peaks, slope and change over time
TextNotes, forms, written reports, voice recordings, videoRadiology, ECG and echocardiogram reportsClinical BERT, 768 numbers per note type; long notes split into 512-token chunks and averaged
ImagesPhotographs, X-rays, CT, MRI, pathology slidesChest X-raysDenseNet121 fine-tuned on CheXpert: 18 diagnosis probabilities and 1,024 features per image, also averaged across all of a patient's images
OtherSocial media, wearable sensors, behaviour, external data; also genomics, proteomics, metabolomicsNot used ("future potential")Any pre-trained model can be added

Each patient file also contained admissions, transfers, provider orders, microbiology cultures, medication administrations, prescriptions, intravenous and fluid inputs, sensor outputs and billing codes. The final model was XGBoost, chosen after comparing logistic regression, decision trees, random forest, multi-layer perceptron, LightGBM and TabNet.

The box HAIM left empty, "other sources" such as wearables and behaviour, is where dementia happens: at home, between hospital visits. That is the box our project wants to fill.

12What I have to think next

How do we integrate all the medical data a marketplace needs to give each person with dementia the best service?

Below is every type of data that came up in this research, grouped the way HAIM groups data, plus the two groups a marketplace adds: care context and the person's own wishes. Integrating them raises the questions I will work on next: which data each matching decision really needs, who consents to sharing what, and how to keep the person's wishes at the start of the chain.

Tabular and profile data one-time or occasional

  • Demographics: age, sex and other demographic variables
  • Hospital records: admissions, transfers, provider orders, prescriptions, medication administrations, microbiology cultures
  • Billing codes: diagnosis and procedure codes
  • Medical history and current medication list
  • Education level
  • Lancet risk factors: hearing loss, high LDL cholesterol, depression, traumatic brain injury, physical inactivity, diabetes, smoking, hypertension, obesity, excessive alcohol, social isolation, air pollution, untreated vision loss
  • Genetics: APOE genotype
  • Blood biomarkers: amyloid beta, phosphorylated tau 217
  • Cerebrospinal fluid tests
  • Omics: genomics, proteomics, metabolomics

Time series in hospital and in daily life

  • Bedside vital signs: heart rate, systolic and diastolic blood pressure, respiratory rate, SpO2, Glasgow Coma Scale (verbal, eye, motor)
  • Lab tests: glucose, potassium, sodium, chloride, creatinine, urea nitrogen, bicarbonate, anion gap, hemoglobin, hematocrit, magnesium, platelet count, phosphate, white blood cells, total calcium, MCH, red blood cells, MCHC, MCV, red cell distribution width, neutrophils, vancomycin
  • Procedures: urinary catheter, PICC line, intubation, peritoneal dialysis, bronchoscopy, EEG, continuous renal replacement therapy, dialysis with catheter, chest tube removal, hemodialysis
  • Other clinical signals: pulse oximetry, blood chemistry, respiratory analysis, ECG, ultrasound, in-vitro tests, intravenous and fluid inputs, sensor outputs
  • Gait from a watch or phone: usual and maximal walking speed, daily steps, cadence, step-time variability, stride regularity, running duration, walking steadiness
  • Daily rhythm and sleep: bedtime, sleep duration and stages, rhythm strength (relative amplitude), fragmentation, day-to-day consistency, timing of peak activity, sedentary time
  • Cardiovascular: heart rate, heart rate variability, blood pressure, 30-day hypertension pattern notifications
  • Hearing: hearing checks and audiograms over time, noise exposure
  • Brain signals: wearable EEG (for example MUSE 2, EPOC X)
  • Home sensors: sleep mats, motion sensors, appliance use
  • Location: outings, routes, getting lost, GPS alerts
  • Phone use: passive device-use patterns, calls and social contact
  • Repeated cognitive tests: monthly and high-frequency smartphone tests, twice-yearly surveys of memory concerns
  • Events: falls, missed medication, agitation episodes, infection risk, hospital admissions, going missing

Text and voice

  • Clinical notes: radiology, ECG and echocardiogram reports; physician and nursing notes; forms; written reports
  • Speech: free conversation of 10 minutes or more, voice patterns, voice recordings
  • Needs assessments: company, daytime activities, information, psychological distress, memory, eyesight and hearing, accidental self-harm, benefits, rated by the person, the carer and professionals
  • Self-reports: surveys, diaries, subjective memory concerns
  • Caregiver reports: observations and caregiver burden measures

Images and video

  • Medical imaging: X-rays, CT, MRI (brain atrophy, white matter lesions), amyloid PET, ultrasound
  • Other images: photographs, pathology slides
  • Smart-glasses data: video, audio and gaze from glasses such as CrossSense and CareYaya

Care context what a marketplace adds

  • Autonomy: rank I to M on the Japanese scale, or very mild to severe
  • Care level: long-term care certification (support 1–2, care 1–5)
  • Living situation: alone, couple only, with family; at home, group home or facility
  • Region: municipality, local services, public programs and subsidies
  • Coverage: insurance, municipal programs, ability to pay
  • Service data: what each provider offers, staffing, availability, price, ratings and reviews
  • External data: social media, behaviour, environment

The person's own wishes the data that makes it personal

  • What they want to keep doing by themselves
  • What they will share, and with whom: family, professionals, companies
  • Consent settings, recorded early while awareness is intact
  • Preferences about company, activities, information and how they want to be reminded

SourcesReferences

  1. MHLW (2024). Dementia and MCI prevalence and projections. mhlw.go.jp
  2. Jiji Press (2024). Ninomiya on MCI and projections. jiji.com
  3. PMDA (2026). Revision of lecanemab and donanemab precautions; APOE testing. pmda.go.jp
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  5. National Police Agency data via Kyodo (2026). sakigake.jp; ANN; nippon.com; CareNet 2024; Jiji 2023; Hiroshima police 2021; Gendai 2022
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  28. CrossSense and the Longitude Prize: Nesta; trial results
  29. CareYaya: TechCrunch; AARP AgeTech; smart glasses
  30. GPS programs and prices: Minato; Bunkyo; Fukuoka; Sanjo; Kofu
  31. Acceptance: wearable survey 2025; privacy in smart homes; meta-synthesis; focus groups
  32. Counsel Health: Series A; a16z; HIT Consultant; seed; chronic care; ACCESS model
  33. Welmo: ASCII; AIsmiley; The Bridge; INITIAL
  34. Lottie: lottie.org; Silicon Canals
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