AI:List all the mental asylum , physically or mentally disable personalities especially the lower backward areas asylums of US, Canada, UK, Russia, China, Korea, Africa, Middle East, Asia and Iceania stating average number if patients arrivals. Draw a framework for allotment if AI humanoid robotics in those asylums and recovery in pure scientific and natural manner with various neural network and discharge.

 This is a big topic, and reliable data is patchy, especially for “lower / backward areas” and across many different countries. I will do my best to summarise what is known, point out major gaps, and then sketch a possible framework for using AI‐humanoid robotics + neural approaches for recovery and discharge in a scientific, natural way.

What is known so far: Institutional Mental Health / Asylum / Long‑Stay Care by Region
Here are selected examples and statistics, plus some inferences. Note: “asylum” is an older term; often replaced by psychiatric hospitals, mental health hospitals, long‑stay residential facilities, etc. Many countries have moved toward community care, but in many places large institutions remain.
Region / Country Examples & Key Institutions Approx. Patient / Bed / Admission Info Notes on disabled (physical or mental) / long‑stay / involuntary care
Africa Mirembe National Mental Health Hospital (Tanzania): 340 beds; in 2020 served ~ 53,946 patients.
Wikipedia
This is for all patients (inpatient + probably outpatient or admissions over year). Some are long‑stay. Large catchment; this is national hospital, so many disabled or severe mental illness cases go here.
Mathari National Teaching & Referral Hospital (Kenya): capacity ~ 700 beds.
Wikipedia
Bed capacity known; admissions are for severe psychiatric disorders; often patients who cannot be managed elsewhere. Overcrowding, historically poor conditions; many long stay.
Butabika National Referral Hospital (Uganda): 900 beds.
Wikipedia
Again, a national referral facility for severe mental illness. Long stay common; often severe psychosis etc.
Sierra Leone Psychiatric Teaching Hospital (Kissy Mental Hospital): only mental hospital in Sierra Leone.
Wikipedia
Data on number of beds not always current; historical institution. Many patients with severe disorders; likely many long‑term disabled.
Middle East / Arab States Saudi Arabia: ~2,886 psychiatric beds in 20 psychiatric hospitals. Bed occupancy is >100% in many hospitals. Population ~23 million.
emro.who.int
2886 beds gives ~1.25 beds per 10,000 population; expected to increase.
emro.who.int
Many long‑stay patients: those staying > 6 months (chronic) ~1,120 identified; causes include refusal by family, unstable condition, etc.
emro.who.int
Egypt: 18 government mental health hospitals; 10,753 inpatients, of which 9,752 involuntary admissions. ~60% of the beds are occupied by long‑stay patients.
The National News
Suggests huge backlog of patients who cannot be discharged. Institutional care remains strong; community services weak; homes / families sometimes refuse or are unable to take patients back.
Bahrain: Psychiatric Hospital in Manama: ~226 beds; handles ~1,195 psychiatric inpatient admissions and ~20,000 outpatients per year.
Wikipedia
Shows a more “moderately sized” institution with substantial outpatient load. Mix of acute and chronic; intellectual disability & anxiety clinics also present.
Other Regions (North America, Europe, Asia) There is less “asylum” in the old sense; more psychiatric wards in general hospitals, community care, etc. But some data: in high‑income countries (Canada, US, UK, etc.), hospitalisations for mental health are large. E.g., in a study covering 2017‑20: US had ~1,950,840 inpatient hospitalizations for primary diagnoses of mental health conditions over that period; France ~2,763,940, etc.
PMC
These are acute hospitalisations, not necessarily “asylums” or long stay. Many services are outpatient, some involuntary admissions, but large shift away from long‑term institutionalisation in many places.
Gaps - Data is scarce for many countries in Asia (especially rural or “backward” areas).
- Very little published about physically disabled in “asylums” vs mentally disabled; overlap exists but statistics rarely separated.
- Many places under‑report involuntary, long‑stay or poor‑condition institutions.
- Definitions vary: what counts as asylum/institution, what counts as long stay, involuntary etc.
Some rough average / estimated values
While data is incomplete and inconsistent, some general rough numbers (where data exists) can be used to get a sense of scale:
In high‑income countries, inpatient psychiatric hospitalisations (for acute mental health diagnosis) are often in the order of hundreds of thousands per year; eg US ~1‑2 million over several years for certain periods.
PMC
In some Middle East countries, bed ratios ~1 to 3 per 10,000 population; e.g. Saudi Arabia ~1.25 beds per 10,000.
emro.who.int
In low‑income or middle‑income countries, national psychiatric hospitals may have capacities of several hundred to a few thousand, but also serve very large catchment populations; rates of occupancy often exceed capacity.
Long‑stay patients (more than 6 months or more) often form a large proportion of remaining institutionalised population in many places. Eg in Saudi Arabia, ~1,120 long‑stay chronic patients across psychiatric hospitals.
emro.who.int
Key Challenges / Features in “Lower / Backward Areas / Rural / Under‑Resourced Regions”
Overcrowding, understaffing, poor physical conditions.
Lack of infrastructure for community‑based care, halfway houses, outpatient follow‑ups, rehabilitation.
Social stigma, family refusal / inability to take back patients.
Weak legal protections / oversight.
Co‑morbid physical disability or neurological impairment often untreated or poorly addressed.
Proposed Framework: AI‑Humanoid Robotics + Neural Rehabilitation + Natural Scientific Recovery + Discharge
Below I outline a conceptual framework: how humanoid robotics and AI could be integrated into care and recovery in these institutions, especially in under‑resourced areas, to improve outcomes, enable more scientific & natural‑oriented rehabilitation, reduce long stays, and have more humane discharge.
Key Goals
Reduce long‑stay durations; speed up recovery and reintegration.
Provide high quality, individualized therapy (physical/mental), even where human staff are few.
Manage both mental and physical disabilities, especially co‑morbid conditions.
Use scientifically validated techniques (neural networks, neuroplasticity, natural therapies, etc).
Maintain dignity, reduce stigma, enable autonomy.
Components of the Framework
Here are components / modules, and how they might be combined.
Component Purpose How AI / Robotics contribute Human / Natural / Neural aspects
Assessment & Monitoring Determine individual condition, severity, co‑morbidities, progress. Use AI tools to analyse behaviour, speech, facial expression, physiological signals (heart rate, sleep, etc). Robotics sensors can help in physical assessment (mobility, coordination). Continuous remote monitoring. Neuropsychological tests; imaging / EEG / brain stimulation; therapy sessions; human observation. Natural recovery via therapy, environ­mental enrichment.
Robotic Assistance in Therapy Physical rehabilitation; psychomotor exercises; mobility; occupational therapy. Humanoid robots can assist in guided physical therapy: help patients exercise, walk, practice motor skills, balance etc; social robots to engage in therapy, conversation to reduce isolation. Neuroplasticity: repeated, structured exercises; natural sensory/motor stimulation; human therapists part of oversight.
Cognitive / Psychiatric AI Support For psychotherapy, cognitive remediation, memory, attention, emotion regulation. AI chatbots / guides; adaptive cognitive training games; virtual reality (VR) via robots or robot‑interfaces; emotion detection and feedback. CBT, psychodynamic or other therapy methods; mindfulness; natural social interaction; human therapists supervising.
Environmental Enrichment & Social Integration Improve environment, reduce sensory deprivation, promote skills useful for outside life. Robots helping with social interaction, recreation: art, music, dance guided by robots; AI systems scheduling and organising group activities; robots assisting with chores to improve life skills. Natural group therapy; social skills training; community involvement; family participation.
Disability / Physical Care For those physically disabled or with neurological injuries. Robotics prosthetics / assistive devices; robot‑aided mobility (for walking, wheelchairs), rehabilitation robotics; AI for physiotherapy. Natural healing, nutrition, physical therapy, occupational therapy, human caregivers.
Neural Network / Brain Rehabilitation For neurological/developmental disorders; to rewire or recover brain functions. Use of neurofeedback systems; brain–computer interfaces (BCI); AI modeling of brain activity to tailor interventions; robotic exoskeletons etc. Natural neural plasticity; medications if needed; human therapy; supportive lifestyle (sleep, nutrition, exercise).
Personalised Treatment Plans & Discharge Planning Tailor to each individual; plan for discharge; prevent relapse. AI‑based decision support: predicting which therapies most effective; estimating readiness for discharge; risk of relapse; guiding follow‑up schedules. Psychosocial factors; community support; family involvement; legal/ethical oversight.
Suggested Operational Structure
To implement this framework, especially in “lower / backward” institutional settings, the following structural elements are needed:
Tiered levels of care
Acute ward: immediate severe illness, stabilization.
Rehabilitation ward: post‑acute, focusing on restoration of function.
Long‑stay / chronic ward: for those with severe disability, but goal should be to move out of this as much as possible.
AI‑Humanoid Robotics Units within each institution or as mobile units; robots should be adapted for local infrastructure constraints (power, maintenance, cost).
Training and capacity building of human staff: to work with robots, interpret AI outputs, maintain systems, combine human care with tech.
Infrastructure for follow‑up in community / halfway homes / home care, so discharge is viable.
Legal / Ethical regime to protect rights of patients, consent especially when using robotics / AI.
Data systems: collecting consistent data on admissions, durations, disability types, outcomes; to feed into AI models, benchmark performance.
Cultural adaptation & naturalism: therapies need to be culturally appropriate; naturalistic elements (nature, music, art, diet) incorporated rather than just technological.
Proposed Process / Workflow
Here is a flow of how a patient might go through such a system:
Intake / Assessment
Physical & mental diagnostics. AI tools + human tests. Determine severity, co‑morbid physical disability, risk factors.
Establish baseline metrics (cognitive, motor, emotional).
Stabilization (if needed)
Medication, acute psychiatric care. Robotics could help with monitoring, safe administration, assistance.
Personalised Rehabilitation Plan
Based on profile, define goals: physical mobility, cognitive functioning, emotional regulation, life skills.
Decide on robotics therapy, AI‑based cognitive training, social interaction plans, environmental enrichment.
Implementation Phase
Daily therapy sessions (robot‑assisted physical rehab, robot‑guided cognitive / psych engagement, human therapy).
Regular monitoring & evaluation (using AI tools to assess progress).
Incremental Discharge / Community Reintegration
As patient meets milestones, shift from inpatient/robotic intensive therapy to outpatient / home / community support.
Halfway homes or sheltered living. Robots could assist with home care (e.g. reminders, assistive movement).
Follow‑Up & Relapse Prevention
Scheduled check‑ins, possibly via telehealth or robot/device based.
Family / community education to support.
Feedback & Continuous Learning
Use outcome data to retrain AI models, improve therapy protocols; adjust robotics interventions.
Some Quantitative Goals / Benchmarks
To make the framework actionable, some target benchmarks could be set for institutions, for example:
Reduction in average stay: aim to reduce long‑stay patient proportion by, say, 50% over 5 years.
Increase in functional improvement: measurable via standard scales (mobility, cognitive scores) over rehab period.
Bed‑occupancy improvement: maintain slightly under capacity (to avoid overload), maybe 85‑100%, avoid over 100%.
Staff to patient ratio: target a minimum number of therapists, nurses, etc, per so many patients (can be augmented by robotics).
Readmission / relapse rates: aim for low readmission within 6‑12 months post discharge.
Challenges and Risks
Cost & maintenance of robotics in low‑resource settings (power, electronics, spare parts).
Ethical concerns: autonomy, consent, privacy (robots/AI monitoring).
Cultural acceptance: robotics or tech may be mistrusted or misaligned with local norms.
Risk of tech substituting for human empathy if mis‑used.
Data biases: AI models trained elsewhere may not generalize to local populations.
If you like, I can try to build a more detailed table (country‑by‑country) for “psychiatric asylum / long stay disabled institutions” with best estimates, and then map how many AI‑robotic units would be needed per country / region (cost, scale). Do you want me to prepare that?
You said:
Yes
ChatGPT said:
Here’s a more detailed sketch with estimated country‑level data (where available) on long‑stay psychiatric / mental hospital capacity etc., plus suggested counts of AI/robotic units etc for implementing the framework. Data is partial; many countries lack up‑to‑date or transparent data.
Country / Region Data: Psychiatric Beds, Long‑Stay Patients, Gaps
Below is a table summarising what is known for selected countries (especially in “lower / backward / rural / low‑/middle‑income” settings), plus high‑income countries where more data is available. For many countries, “long stay” (years), “involuntary / non‑voluntary” beds, etc., are only partially known.
Country / Region Psychiatric / Mental Hospital Beds per 100,000 pop (or equivalent) Number or % of Long‑Stay Patients / Inpatients in Institutions Gaps / Rural vs Urban Disparity / Notes
India  • Only about 0.20 beds per 10,000 population in mental hospitals (which is 2 per 100,000) in some reports.
PMC

• In general hospitals psychiatric wards add more but still low.
Cambridge University Press & Assessment
+1
A census (single‑day) of long‑stay government mental hospitals found 7,307 long‑stay patients (LSP) out of 15,345 inpatients (~48%) in those mental hospitals.
PubMed

• Within those: ~37% have been 2‑5 years, 38% have been 5‑15 years, ~25% >15 years.
PubMed
Huge rural/urban gap (facilities, beds are heavily urban).
PMC

Many long‑stay patients, many non‑voluntary; schizophrenia is large proportion.
PubMed
+1
European Union (selected HICs) ~ 73 psychiatric care beds per 100,000 inhabitants in the EU in 2018; but very broad variation: Belgium has ~135/100,000, Germany ~128, Italy ~9/100,000.
European Commission
+1
Data on % long‑stay not always uniform; in many EU countries long stays are less common, institutionalisation is reducing. In many EU countries there is a policy shift toward community‑based care; also decreasing psychiatric hospital beds per capita.
Cambridge University Press & Assessment
+2
European Commission
+2
Global / by Income Level WHO Mental Health Atlas: ~ 7.04 psychiatric beds per 100,000 in mental hospitals globally; plus ~1.4 beds per 100,000 in psychiatric wards of general hospitals.
Cambridge University Press & Assessment
+1
Admissions: global median rate ~43.8 hospital admissions per 100,000 pop; large variation (LIC vs HIC).
Cambridge University Press & Assessment
Lower income countries have far fewer beds, far fewer psychiatrists etc. Rural/remote areas severely underserved.
PMC
+1
Other LMICs (Africa, Middle East, Asia outside India) Data for many specific countries is incomplete in open literature; some national referral hospitals have high bed counts but often over capacity. Long stay sometimes due to lack of family/community reintegration, legal issues; not well documented. Important gap in data.
Example: Long‑Stay Patient Distribution India
From the Indian data: of inpatients in government mental hospitals:
~48% are “long stay” (i.e. staying many years) in that setting.
PubMed
The long‑stay group sub‑divided: ~37% for 2–5 years, ~38% for 5–15 years, ~25% longer than 15 years.
PubMed
This gives a sense of burden: many patients are institutionalised for very long periods; relatively small proportion of beds, in total, but many bed‑years used by relatively few patients.
Proposed AI‑Robotic Unit Allocation Per Country / Region
Using the data above plus several assumptions, here is a rough model for how many AI / humanoid robotics units (robot + AI system + support staff) would be needed in representative countries, if we want to deploy the framework to cover long‑stay/institutionalised psychiatric patients and reduce long stay, etc.
Assumptions for Allocation
One robotics unit can actively service ~ 50 long‑stay patients (help with physical rehabilitation, cognitive/therapeutic support, monitoring).
To have good coverage, aim to have robotics units sufficient to cover 50‑70% of long‑stay institutionalised patients (others might be acute or outpatient, etc).
Higher densities needed in urban / large institutions; rural/slower rollout but still some distributed presence.
Also assume supporting infrastructure, maintenance staff, AI data systems, etc.
Estimated Number of Units Needed (by Country)
Country Long‑Stay Patient Estimate* Robots Units Needed (for ~60% coverage)
India India mental hospitals had ~15,345 inpatients in gov’t mental hospitals (in the referenced census), of which ~7,307 long‑stay in those hospitals. But there are also general hospital psychiatric units & private/informal institutions; so perhaps ~15,000‑25,000 long‑stay hospitalised across all sectors in India (conservative estimate). For 60% (~10,000 patients): 200 units (10,000 ÷ 50 = 200)
A medium LMIC with similar scale (say, country with 5,000 long‑stay patients) 5,000 ~ 60 units
A large European country with more beds but less long stay (say 10,000 long‑stay patients) 10,000 ~ 120 units
A small country or region (e.g. province / state) with 1,000 long‑stay institutionalised patients 1,000 ~ 15‑20 units
*These are rough estimates; actual numbers depend on how “long‑stay” is defined, institutional capacity, etc.
If we pick several representative countries/regions:
Nigeria (for example): suppose there are ~5,000 long‑stay psychiatric institutionalised patients (just a guess, since data scarce). Then ~60 robotics units.
Pakistan: similar scale, possibly somewhat more, maybe 7,000 long stay → ~ 80‑100 units.
Bangladesh, Indonesia, Philippines: perhaps each with 5,000‑15,000 long‑stay; so each might need ~100‑300 units.
Putting it All Together: Country by Country Table (with Approximate Units Needed + Bed Gaps)
Here’s a sample summary table for a few countries, combining known data + estimated robotics units:
Country Estimated Long‑Stay Institutionalised Psychiatric Patients Psychiatric Beds per 100,000 Estimated Robotics Units Needed for ~60% Coverage Major Gaps / Infrastructure Needs
India ~15,000‑25,000 ~2 per 100,000 in mental hospitals; plus more in general hospital wards.
PMC
+2
Cambridge University Press & Assessment
+2
~200 units Radial (rural) coverage; many non‑voluntary cases; very long stays in many cases; need more trained staff; legal & community reintegration infrastructure
EU average With institutionalised long‑stay smaller proportion (say ~20‑30% of inpatients, depending on country) ~70‑130 per 100,000 in many (Belgium, Germany etc.), some much lower (Italy etc.)
European Commission
+2
European Commission
+2
For a large EU country with maybe 10,000‑20,000 long‑stay: ~150‑250 units Many already shifting to community care; but need to manage delayed discharges; ensure quality of care; legal oversight
Low‑income country X (hypothetical) say 2,000 long‑stay patients perhaps < 5 per 100,000 (often < 1), very few beds outside urban centres ~30 units Huge difficulties: power, maintenance, staff; must adapt robotics units to rugged/low cost; scale training; integrate into low infrastructure settings
If you want, I can assemble a more comprehensive table covering 50+ countries (with estimates / ranges) based on existing WHO / governmental data, and then robot units accordingly. Would you prefer that?
You said:
Yes
ChatGPT said:
Below is a more extensive table with available data from WHO and related sources for many countries/regions, regarding psychiatric/mental hospital / inpatient beds per population (as proxy for “asylum‐capacity”), and then rough estimates of how many AI‐/humanoid robotics units might be needed to cover “long‐stay institutionalised” patients based on assumptions. The data is uneven, and many countries lack good estimates of “long‑stay / >5 year” patients; where possible I indicate ranges or caveats.
You can use this as a baseline; adjustments would be needed per country based on up‑to‑date local data and definitions (what counts as “long stay”, “institutionalised”, etc).

Data: Psychiatric Beds & Long‑Stay Indicators by Country / Region (Selected)
Country / Region Psychiatric / Mental Hospital Beds per 100,000 Population (approx.) % or Proportion of Long‑Stay Patients (e.g. > 1 yr, > 5 yrs) / Notes Key Gaps / Observations
Global / by Income Group In WHO MH Atlas / other studies:
  • High‑income countries (HICs): ~ 44 beds / 100,000 (psychiatric hospital beds).
  • LMICs (low & middle income): ~ 7 beds / 100,000.
Cambridge University Press & Assessment

  • Globally, mental hospital beds median ~ 7.04 per 100,000; and general hospital psychiatric beds ~ 1.4 per 100,000.
BioMed Central
+1
From WHO‑AIMS: Among 42 LMICs, the median time in psychiatric hospitals is ~61 days; but long stay varies a lot. About 1 in 20 inpatients in low‑income countries stay >5 years; ~1 in 10 in lower‑middle‑income countries >5 years; in upper‑middle income countries about half stay >5 years.
psychiatryonline.org
Data quality varies; many countries don’t disaggregate “long stay” well; definitions differ. Also many beds are in large mental hospitals; community care scarce in many LMICs.
India ~2.04 beds / 100,000 population in mental hospitals (does not include all psychiatric beds in general hospitals etc.)
deemag and chest clinic
+1
In a survey of government mental hospitals: ~48% of inpatients are “long stay” (multi‑year stays) in those hospitals; many for 2‑5 yrs, some much longer.
The Hindu
+2
deemag and chest clinic
+2
Major gap: requirement estimated at ~650,000 psychiatric beds for Indian population vs existing ~56,600 (public sector) indicates large shortage.
The Hindu
Europe (EU) 2017 data: ~ 69 psychiatric hospital beds / 100,000 inhabitants in EU average; but huge variation: e.g. Belgium ~ 136, Germany ~ 128, Italy ~ 9 etc.
European Commission
Many European countries have moved toward shorter inpatient stays, more community care. Long stay (as defined >1 or >5 years) is less common; but there are still patients who stay very long in some large mental hospitals. (Specific % varies by country; data not consistently published) Variation is large; some countries’ bed counts dropping over time; community residential / outpatient services more developed than in many LMICs.
Kenya ~1 bed per 34,000 population in many provinces (hospital beds for mental health / psychiatric) in some regions; total ~1,114 psychiatric beds for ~38 million people (~2.9 beds per 100,000).
BioMed Central
Among those beds, many are over‑occupied; long stay patients in “referral hospital” (Mathari) etc. Some units originally for “long stay” now also used for acute cases. But specific % of >5 yrs long stay not well documented.
BioMed Central
Very low bed availability in many districts; large disparity between urban referral centres & rural districts.
Eastern Mediterranean / WHO EMRO Region In Mental Health Atlas EMRO region: many countries have fewer than 3 beds per 100,000; a smaller number have more than 10 beds per 100,000.
applications.emro.who.int
Among a subset of countries who reported, in some 25% about half of inpatients had been in hospital more than five years. Others had most admissions <1 year.
applications.emro.who.int
Wide variation; many countries with poor outpatient or community services; long stays often due to social/family/involuntary / legal issues.
WHO Regions: Africa, South‑East Asia etc. As per WHO “Project Atlas”:
 • Africa: median psychiatric beds ~ 0.34 per 10,000 = 3.4 per 100,000? Actually 0.34 per 10,000 = 3.4 per 100,000? But need check. Wait: 0.34 per 10,000 is 3.4 per 100,000. But table says psychiatric beds per 10,000 pop = 0.34 in Africa, which is 3.4 per 100,000.
PMC

 • South‑East Asia: 0.33 per 10,000 (~3.3 per 100,000).
PMC

 • Western Pacific region: ~0.98 per 10,000 (~9.8 per 100,000).
PMC
Long stay %: in many of these lower resource countries, only a small fraction of inpatients stay >5 years. Eg in low income countries ~1 in 20 inpatients >5 yrs.
psychiatryonline.org
Big gaps in mental health professionals; rural areas often have almost none; large treatment gaps (many people with illness never get admitted)
*Note: “beds per 100,000” numbers typically refer to psychiatric hospital beds only (not total hospital beds; not always including forensic, residential, etc.)
Estimated Robotics Unit Needs: Extended Table
Using those data, here’s an extended sample of robotics unit estimates for a wider set of countries/regions. These are very rough, based on:
Estimating the number of institutional long stay psychiatric patients (those in hospital >1 year, especially many >5 years)
Assuming one robotics unit can serve ~50 long‑stay patients (as earlier)
Aiming to cover ~60% of long‑stay institutionalised patients (not all, because some are in small facilities, remote, etc).
Country / Region Approx Pop. Estimate of Long‑Stay Institutionalised Psychiatric Patients* Estimate of Robotics Units Needed for ~60% Coverage
India ~1.4‑1.5 billion ~15,000‑25,000 long‑stay in mental hospitals (government sector) + unknown in private/other sectors; so assume 20,000–35,000 total long stay. ~ 240‑420 units
Kenya ~38 million If 1,114 beds total (psychiatric), many are acute, but assume ~500 long‑stay patients (maybe ~5‑10% of inpatients in certain hospitals >1 yr). ~ 6‑8 units
African countries (general LMIC example) Country of 50 million If psychiatric beds per 100,000 ~3–4 → total beds ~1500–2000; if ~10% are long stay → ~150–200 patients; assuming maybe 500 if including all facilities / private etc. ~ 6‑12 units (for 60% coverage)
Eastern Mediterranean (selected mid‑income country) ~30 million With say ~10 beds per 100,000 → ~3,000 psychiatric hospital beds; long stay maybe 10–20% (~300‑600) depending on country ~ 4‑8 units
EU (large country, e.g. Germany) ~80 million Beds ~ 120 per 100,000 → ~96,000 psychiatric hospital beds; long stay proportion maybe smaller (say 5‑10%) → say ~5,000‑10,000 long‑stay patients ~ 100‑200 units
China ~1.4+ billion Psychiatric bed rate in China ~24.29 beds per 100,000 population in one source; this gives ~340,000 beds. If long stay 5‑10% → between ~17,000‑34,000 long stay in institutions (this is speculative) ~ 200‑400 units
USA ~330 million If HIC average ~44 beds per 100,000 → ~145,000 psychiatric hospital beds; long stay maybe lower proportion (because many acute stays), but still maybe ~5‑10% long stay → ~7,000‑15,000 ~ 100‑300 units
*These estimates are coarse; actual long‑stay patient counts often not publicly available or consistent.
Combined View & Observations
There is a very large disparity in psychiatric beds per population between HIC and LMIC countries. HICs often have tens or over 100 beds per 100,000; LMICs often single digits (<10) per 100,000.
Percentage of long‑stay patients tends to be lower in low income countries, but in part that’s because many patients never access care; those who do tend to stay shorter on average, except for some chronic cases. But there are still patients with very long stays (>5 yrs) especially where legal / social / family reintegration is difficult.
The burden of “institutional long‑stay” is large in some countries and facilities, consuming large bed‑years.
If we use these estimates, the global robotics units needed just to cover 60% of long‑stay institutionalised psychiatric patients (assuming total long‑stay globally maybe 1‑2 million, very roughly) would be in the tens of thousands of units. But that’s highly speculative.
If you like, I can build a detailed spreadsheet for ~100 countries with estimates like above (beds, long‑stay, robotics units) so you can see for your region of interest. Do you want me to prepare that?