Measuring Quality and Outcomes in Austrian Long-Term Care

Austria can count how many people use mobile long-term care, how many places exist across residential and related services, how many full-time-equivalent workers are employed and how much Länder and municipalities spend. Those figures are indispensable for planning. They do not, by themselves, reveal whether an older person maintained mobility, whether family-carer burden reduced, whether a residential service protected autonomy or whether somebody receiving support at home actually experienced a good life.

This distinction is central to the Austria Ageing, Long-Term Care & Community Support Knowledge Hub. Austrian long-term care already contains multiple quality mechanisms: federal Pflegegeld rules, Länder legislation and oversight, the Pflegefonds, national service statistics, quality-assurance visits in home-based care and the voluntary Nationales Qualitätszertifikat für Alten- und Pflegeheime. The challenge is not an absence of information. It is that different parts of the system measure different dimensions for different purposes.

Austria’s next quality-development task is therefore to connect activity, safety, lived experience and outcomes more coherently without imposing a crude national ranking on a deliberately decentralised system. The stronger question is not simply whether services comply with requirements or deliver the expected volume. It is whether support produces the outcomes that matter to people while remaining sustainable for workers, families and public systems.

Austria already has a substantial long-term care data infrastructure

The Pflegedienstleistungsstatistik is one of Austria’s most important national sources for understanding formal long-term care provision. It is grounded in the Pflegefondsgesetz and the Pflegedienstleistungsstatistik-Verordnung and brings together annual data supplied through the Länder.

The statistics cover seven defined service areas where services are publicly co-financed: mobile care and support, multi-hour everyday assistance and respite, day care, residential care, short-term residential care, alternative forms of housing, and case and care management.

For 2024, the national statistics recorded more than 140,000 users of mobile care, close to 86,000 places across residential, day, short-term and alternative-living categories, more than 53,000 full-time-equivalent workers and expenditure of approximately €5.74 billion.

These data are essential. They show scale, capacity, workforce distribution, expenditure and variation between Länder. They can reveal whether one service category is expanding or contracting and support the Sicherungs-, Aus- und Aufbaupläne that Länder submit under the Pflegefonds framework.

But they remain principally service-system data.

They tell Austria a great deal about what is being delivered. They tell it less consistently about what happened to the person because of that delivery.

That is the distinction between quality data and performance metrics and outcome measurement. Both matter, but they answer different questions.

Activity is not the same as quality

A mobile service can increase the number of visits it delivers without necessarily improving independence. A Pflegeheim can maintain high occupancy and low incident rates while residents gradually lose mobility or participation. A Land can increase expenditure substantially without knowing whether additional resources reduced unmet need.

None of these activity measures is unimportant.

The problem arises when they become proxies for quality simply because they are easier to collect.

A mature quality framework distinguishes at least four dimensions:

• structure: workforce, facilities, equipment, governance and service availability;
• process: assessment, care planning, medication management, review, response and communication;
• experience: dignity, choice, trust, relationships, participation and whether the person feels heard;
• outcomes: what changes, improves, stabilises or deteriorates in the person’s life and health.

Austria already measures aspects of all four, but not through one integrated national long-term care outcome framework.

That is partly a consequence of federalism. Länder regulate many aspects of residential and community services, and different providers use different quality-management approaches. It is also a conceptual challenge: long-term care outcomes are inherently more difficult to compare than simple activity counts.

Quality in home care is measured through the actual living situation

Austria’s Qualitätssicherung in der häuslichen Pflege provides an important example of quality measurement that moves beyond administrative service records.

Since 2005, free quality-assurance home visits have been undertaken nationally for people receiving Pflegegeld in home-based arrangements. Visits are voluntary for many Pflegegeld recipients and compulsory for recipients of public support for 24-Stunden-Betreuung. The system is coordinated through the Kompetenzzentrum Qualitätssicherung in der häuslichen Pflege within the Sozialversicherungsanstalt der Selbständigen.

Qualified nurses assess the actual support situation rather than merely confirming that a care arrangement exists.

The assessment considers six areas:

• the functional housing situation;
• personal care;
• medical and nursing support;
• nutrition and hydration;
• household hygiene; and
• activities, occupation and social life.

Each area can be assessed from a situation in which needs are reliably met through to circumstances where physical or mental health is already adversely affected.

This is significant because it recognises that quality at home cannot be inferred from Pflegegeld level, family availability or the number of service hours purchased.

The relevant evidence is what life actually looks like in the household.

Operational scenario: a care arrangement looks stable until somebody observes it properly

An 87-year-old man in Lower Austria receives Pflegegeld and is supported principally by his wife, with a mobile service attending several times each week. From an administrative perspective, the arrangement appears stable. His benefit is in payment, the service attends and there have been no recent hospital admissions.

During a quality-assurance home visit, a qualified nurse identifies a different picture.

His wife is managing increasingly complex medication routines, both are eating irregularly and he has stopped leaving the house. Personal care remains adequate, so there is no dramatic safeguarding event. Yet his social activity, nutrition and his wife’s ability to sustain the arrangement are deteriorating.

The value of the visit lies partly in seeing these dimensions together.

The response may include advice, connection with relevant services, review of care arrangements and support for the wife. If similar patterns appear repeatedly across households, they also become system intelligence about where nominally stable home-care arrangements are carrying hidden risk.

This illustrates why quality monitoring systems need direct evidence from people’s actual circumstances. Administrative stability should never be assumed to mean good outcomes.

Organisations examining comparable evidence gaps can use the Quality Dashboard Builder to bring together operational, quality, workforce and outcome measures rather than interpreting isolated indicators as proof of service quality.

Residential care is governed through Länder requirements, with an additional national quality layer

Austria’s residential long-term care system reflects the country’s federal structure.

The Länder regulate requirements affecting the operation, staffing, organisation and quality of Alten- und Pflegeheime through their respective legislation and oversight arrangements. This means structural and regulatory requirements are not identical in every Land.

Nationally, the Nationales Qualitätszertifikat für Alten- und Pflegeheime, or NQZ, provides an additional quality-development mechanism.

The NQZ is jointly supported by Bund and Länder and operates as a voluntary external assessment rather than as a replacement for statutory regulation. It is intended to recognise organisations that demonstrate systematic quality development beyond minimum legal requirements.

Its focus is particularly relevant to outcome thinking because it considers how structures, processes and results contribute to residents’ quality of life, participation and the working environment.

Certification teams examine whether everyday organisational processes reflect residents’ needs, whether resources are used effectively and how the organisation learns and improves.

The important distinction is that the NQZ does not replace Land-level inspection. Its role is developmental rather than constituting the single mandatory national regulatory standard for every Pflegeheim.

The voluntary nature of the NQZ is both a strength and a limitation

Voluntary quality certification can encourage ambition.

Organisations that participate are invited to demonstrate not only that they meet legal requirements but that they actively develop quality and can explain why their systems improve life for residents and staff. External assessment and subsequent recertification can create a disciplined improvement cycle.

Yet voluntary participation also means NQZ evidence cannot automatically be treated as a complete national picture of residential care quality.

A certified home may demonstrate strong quality-development maturity. The absence of certification does not, by itself, prove poor care. Providers may choose different recognised quality systems or focus resources elsewhere.

This creates an important measurement principle: Austria should avoid confusing participation in a quality mechanism with the outcome being measured.

The strongest national framework needs both mandatory baseline assurance and credible ways of examining whether people experience good outcomes above that minimum.

Outcome measurement is difficult because long-term care often aims to maintain rather than improve

Healthcare measurement often looks for recovery: an infection resolves, a wound heals or a clinical value improves.

Long-term care is more complex.

A person with progressive dementia may experience excellent support even while cognition declines. A frail resident may lose mobility despite skilled care. Someone with a degenerative neurological condition may require increasing assistance regardless of service quality.

The outcome therefore needs to be interpreted against the person’s trajectory and goals.

Maintaining function may represent success. Slowing deterioration may represent success. Preserving comfort, dignity or social connection may be a meaningful outcome even where physical dependency increases.

This is why Austrian work on Pflege-Ergebnisqualität has historically identified a broad range of potential indicators rather than one single measure. Relevant domains have included falls, nutrition, skin integrity, infections, mobility, continence, medication, pain, self-care ability, psychological wellbeing, communication, prevention and satisfaction.

The underlying challenge remains current: useful indicators must be clinically and operationally meaningful while also being sufficiently standardised to allow learning across organisations.

Outcome measurement should support goal-led and outcomes-focused support, not penalise services for supporting people whose conditions naturally progress.

Operational scenario: declining mobility does not automatically mean declining quality

An 89-year-old woman living in a Pflegeheim in Styria has Parkinson’s disease and significant frailty. During twelve months, her ability to walk independently deteriorates despite physiotherapy, appropriate equipment and regular support.

If mobility alone is used as the outcome measure, the service appears to have performed poorly.

A more complete analysis looks at the trajectory.

The woman has remained free from serious falls, continues to walk short distances with support, participates in meals outside her room and retains involvement in activities she values. Her pain is well controlled and she has been involved, with her family where appropriate, in decisions about changing mobility support.

The clinically realistic objective was never to preserve unrestricted walking indefinitely.

Quality lies in maximising safe function and participation within a progressive condition.

This is why outcome evidence should combine measures rather than turn a single indicator into a judgement on the whole service. Mobility, falls, pain, participation, choice and the person’s own priorities all help explain whether deterioration has been managed well.

The wider principle of person-centred planning for older people is essential here. Outcomes need to reflect the individual rather than only an average expected pathway.

Falls are useful quality indicators only when interpreted intelligently

Falls illustrate the difficulty of long-term care measurement particularly well.

A service with fewer falls may appear safer. But the figure is not meaningful without understanding the population, mobility levels and restrictions used to achieve it.

A Pflegeheim could theoretically reduce falls by discouraging residents from walking. That would improve one indicator while potentially worsening independence, strength, social participation and quality of life.

Conversely, a service supporting residents to remain active may experience some falls despite good assessment and prevention.

Outcome measurement therefore needs balance.

Useful falls evidence can include frequency, injuries, recurrent patterns, circumstances, medication factors, whether risk assessment changes appropriately and whether residents maintain mobility.

The relevant question is not “Were there zero falls?”

It is whether foreseeable risk was understood, proportionate preventive action was taken and people remained as active as reasonably possible.

This brings together falls, medicines and frailty with autonomy rather than treating incident reduction as the sole definition of safety.

Family-carer outcomes need greater visibility

Austria’s long-term care system depends heavily on informal care. Measuring only outcomes for the person receiving support therefore leaves part of the system invisible.

A home-care arrangement may enable an older person to remain at home while simultaneously causing severe exhaustion for a spouse or forcing an adult daughter to leave employment.

The person-level outcome may appear positive. The household outcome is more ambiguous.

This matters particularly because family care is not evenly distributed. Women provide a disproportionate share, and many carers combine employment, their own health needs and other family responsibilities.

Quality evidence should therefore examine whether formal support is stabilising or intensifying carer burden.

Relevant measures may include carers’ confidence, ability to take breaks, sustainability of working arrangements, access to replacement care, psychological strain and whether the family member wishes to continue providing the same level of support.

This does not make families subjects of surveillance.

It recognises that carer support and family partnership are part of care quality rather than merely private household matters.

Operational scenario: successful home care masks an unsustainable family outcome

An 84-year-old woman with dementia lives at home in Vienna with support from her husband, a mobile service and regular involvement from their daughter.

From the perspective of service activity, the arrangement is stable. Visits are delivered, medication is managed and there have been no serious incidents.

A review of broader outcomes reveals that her husband has stopped attending his own medical appointments because he is afraid to leave her alone. Their daughter has reduced working hours and reports repeated night-time calls from her father.

The woman has successfully remained at home, but the arrangement is becoming unsustainable.

A stronger quality response does not wait for carer breakdown.

Formal support can be reviewed, respite or other options explored, and the family’s actual capacity discussed openly. If the woman remains at home, the outcome should include whether the arrangement becomes more sustainable for those supporting her.

This illustrates why “remaining at home” is an incomplete outcome measure.

A good long-term care system needs to know whether the home arrangement remains safe, acceptable and sustainable for the person and the people whose unpaid work makes it possible.

Experience measures are essential because some aspects of quality cannot be inferred from records

Long-term care is relational.

Documentation can show that personal care occurred. It cannot fully show whether the person experienced that care as respectful. A record can confirm that food was offered without showing whether meals support dignity, culture and preference. A care plan can document activities without demonstrating that a resident feels their day has meaning.

This is why experience evidence matters.

Residents, people using mobile services and family members can identify issues invisible to operational metrics: rushed care, inconsistent workers, poor communication, excessive waiting, loss of privacy or a service that technically meets needs but leaves people without real control.

Feedback should therefore form part of service-user feedback and co-production, not operate solely as satisfaction surveying.

The distinction matters.

Satisfaction can be influenced by expectations and reluctance to criticise workers on whom somebody depends. More meaningful experience measures ask specific questions about choice, continuity, communication, dignity, participation and confidence in raising concerns.

Workforce measures need to connect staffing with outcomes

Austria already has increasingly detailed information about the number of people working in long-term care and the full-time-equivalent capacity within publicly supported services.

Those data are essential for workforce planning, but headcount alone cannot show whether staffing is sufficient for good care.

Quality depends on skill mix, continuity, vacancies, sickness, turnover, supervision, competence and workload.

A residential service may meet a formal staffing requirement yet depend heavily on overtime or inexperienced temporary cover. A mobile service may technically employ sufficient workers but lose substantial capacity through travel and fragmented scheduling.

Outcome measurement should therefore connect workforce instability with care experience and safety.

Indicators such as unfilled shifts, overtime, sickness, staff turnover and continuity become more useful when analysed alongside medication incidents, delayed visits, complaints, falls or resident experience.

The goal is not to prove that one staffing number guarantees quality. It is to understand how workforce conditions shape outcomes.

Regional comparison is necessary, but crude league tables would mislead

Austria’s Länder differ in population density, geography, provider mix, service history and care models. National data show substantial variation in the number of people using mobile services, residential capacity, workforce and expenditure.

Some of this variation is expected and legitimate.

Vienna should not be expected to organise long-term care identically to Tyrol or Vorarlberg. A rural district may need more workforce time per home visit because travel is structurally greater. A Land with a stronger tradition of community-based services may show a different residential pattern from one with historically greater institutional provision.

Comparative quality data therefore need contextualisation.

Useful national comparison should ask whether differences in service configuration lead to materially different outcomes, access or burden — not whether every Land produces identical activity ratios.

Potentially valuable questions include:

• how long people wait for appropriate services;
• whether people receive their preferred form of support;
• how often care is provided outside the person’s locality;
• whether hospital use differs for comparable populations;
• how workforce instability varies geographically;
• whether family burden is greater where formal services are less available; and
• whether functional or quality-of-life outcomes differ systematically.

This would make regional variation more transparent without turning federal diversity into a simplistic competition.

Operational scenario: expenditure rises but the Land still cannot answer whether outcomes improved

A Land increases long-term care spending substantially over three years. Additional funding supports mobile-care capacity, workforce measures and residential provision.

The political and administrative case is straightforward: more money has entered the system and more capacity has been funded.

A later review asks whether people actually experienced better outcomes.

The available data can show expenditure, staffing, service hours and numbers of people supported. It is much harder to answer whether waiting times fell, whether more people remained at home when that was their preference, whether carer burden reduced or whether residents experienced stronger continuity.

The Land therefore begins to connect existing administrative information with a smaller set of outcome and experience measures rather than creating hundreds of new indicators.

Mobile services track continuity, unmet requests and selected functional outcomes. Residential providers contribute comparable information on falls with harm, mobility, experience and workforce stability. Family-carer evidence is sampled rather than assumed.

The aim is not to attribute every change directly to the additional funding.

It is to make the relationship between investment and lived results more visible.

The Digital Twin Scenario Modeller can support organisations examining similar planning questions by testing how different assumptions about workforce, capacity and demand could affect service stability before changes are implemented.

Data quality becomes more important as outcome systems become more sophisticated

Better measurement can create false confidence if the underlying data are poor.

Different providers may define an incident differently. One service may count every assisted lowering to the floor as a fall while another records only unplanned falls resulting in injury. Functional assessment tools may vary. Satisfaction surveys can use different scales and populations.

Without common definitions, comparison becomes unreliable.

Austria therefore needs proportional standardisation.

The objective should not be to force every provider onto one complete care-record system. It should be to create common definitions for a limited number of indicators where comparison has genuine value.

That also requires attention to missing data.

If a provider reports extraordinarily low incident rates, that may reflect excellent practice. It may also reflect under-reporting. If only highly engaged families complete experience surveys, results may not represent the wider population.

Strong digital records and information governance can improve the reliability of quality evidence, but technology does not remove the need for common definitions, professional judgement and validation.

Quality dashboards should support judgement rather than replace it

Long-term care organisations increasingly have the capability to display large numbers of indicators in real time.

This is useful only if decision-makers know which measures matter.

A dashboard containing 80 green indicators can create less assurance than a focused view showing eight meaningful measures and explaining why one is deteriorating.

The strongest governance approach combines leading and lagging indicators.

Lagging indicators show what has already happened: serious falls, complaints, hospital transfers, staff departures. Leading indicators may identify emerging risk earlier: increasing overtime, missed reviews, declining participation or rising short-notice sickness.

Organisations also need thresholds that trigger interpretation rather than automatic judgement.

An increase in falls should prompt analysis of who is falling, with what consequences and under what circumstances. It should not automatically trigger a blanket restriction on mobility.

Quality intelligence is therefore a decision-support function.

For organisations wanting to examine whether their assurance arrangements actually connect information with accountability, the Governance Maturity Assessment can help structure responsibility, escalation and oversight without replacing Austrian regulatory requirements.

Digital interoperability could strengthen outcome measurement across care boundaries

Austria’s long-term care outcomes often depend on interactions with healthcare.

A resident’s repeated hospital transfers may reflect clinical complexity, but they may also reveal limited access to community medical support. A person discharged home may experience functional decline because rehabilitation, mobile care and primary-care follow-up did not connect effectively.

Outcome evidence therefore needs to cross organisational boundaries.

Austria’s developing eHealth infrastructure, including ELGA and broader interoperability work, creates potential for better information exchange where lawful and appropriate. Yet long-term care data have different purposes from clinical records and should not simply be absorbed into health systems indiscriminately.

The stronger opportunity lies in making selected relevant information usable across interfaces.

For example, repeated emergency transfers, medication changes or discharge events can be understood alongside care needs and functional outcomes. In the opposite direction, observations from mobile care may reveal deterioration that is clinically relevant.

This is where interoperability and system integration can strengthen quality intelligence rather than merely digitise information exchange.

Austria should resist the temptation to create one national quality score

A single headline score is attractive because it promises clarity.

Long-term care rarely supports that simplification.

A service could perform very strongly on resident experience and workforce continuity while having a high rate of falls because it supports a particularly frail population to remain mobile. Another could show low hospital transfer rates because residents have effective advance-care planning and palliative support rather than because illness is less common.

Compressing these dimensions into one number hides the explanation.

A stronger Austrian model would use a balanced set of measures with contextual information.

Nationally comparable indicators should focus on issues where shared definitions are credible. Länder should retain the ability to examine additional measures relevant to local service models. Providers should collect person-level outcomes that reflect individual goals.

This creates three complementary levels of measurement rather than one universal score.

The next quality frontier is measuring what people retain

Much long-term care measurement focuses on what services do to or for people.

A more person-centred system also asks what people retain: mobility, decision-making, relationships, everyday skills, social participation, privacy and identity.

This is particularly important in ageing societies.

Care can become unintentionally dependency-producing when efficiency favours doing tasks for people rather than enabling them to remain involved. Measurement can either reinforce or challenge that pattern.

If providers are judged only on task completion and incident avoidance, they have little formal incentive to measure retained capability. If independence and participation become visible outcomes, the purpose of everyday support changes.

This is closely aligned with evidencing person-centred care: records should demonstrate not merely that a service was delivered but how support contributed to the person’s chosen life.

International learning: measure the system without losing the person

Austria offers an instructive case for other decentralised long-term care systems.

It has nationally standardised elements such as Pflegegeld and the Pflegedienstleistungsstatistik, Land-level regulation of many services, nationally coordinated home-care quality visits and a voluntary national residential-quality certificate.

The architecture demonstrates that national measurement and regional autonomy are not mutually exclusive.

The transferable lesson is to be clear about what each layer of evidence is for.

National statistics support planning and comparison. Regulation establishes minimum expectations. External quality systems can support organisational development. Person-level outcome and experience data reveal whether support is achieving what matters in everyday life.

Problems emerge when one layer is expected to answer every question.

Other systems could adapt this principle without reproducing Austria’s Pflegefonds, Länder responsibilities or Pflegegeld model: use administrative data for scale, assurance data for safety and implementation, and outcome evidence for the effect on people.

Conclusion

Austria already measures a great deal about long-term care. National service statistics provide detailed evidence on activity, staffing, capacity and expenditure. Länder oversee quality through their own legal and administrative structures. Home-care quality visits examine the real circumstances of Pflegegeld recipients, while the NQZ provides a national developmental framework for participating residential services.

The next challenge is not simply to collect more data. It is to connect existing information with a clearer account of outcomes. Safety, mobility, comfort, participation, family sustainability, continuity and the person’s own experience need to become visible alongside service volume and expenditure.

That requires disciplined definitions and proportionate national comparability without erasing legitimate Länder variation. It also requires recognising that long-term care outcomes are contextual: maintaining function may be success, deterioration may occur despite excellent care, and low incident rates do not automatically prove a good quality of life.

Austria’s strongest quality system will therefore be one that uses evidence to ask better questions rather than produce ever more reassuring numbers. The central test is whether national funding, regional planning and everyday service delivery can demonstrate not only what care was provided, but what that care enabled people to retain, experience and achieve. Quality becomes meaningful when measurement reconnects the system with the life being supported.