- Research article
- Open Access
Home care quality indicators based on the Resident Assessment Instrument-Home Care (RAI-HC): a systematic review
BMC Health Services Research volume 20, Article number: 366 (2020)
One way of measuring the quality of home care are quality indicators (QIs) derived from data collected with the Resident Assessment Instrument-Home Care (RAI-HC). In order to produce meaningful results for quality improvement and quality comparisons across home care organizations (HCOs) and over time, RAI-HC QIs must be valid and reliable. The aim of this systematic review was to identify currently existing RAI-HC QIs and to summarize the scientific knowledge on the validity and reliability of these QIs.
A systematic review was performed using the electronic databases PubMed, CINAHL, Embase, PsycINFO and Cochrane Library. Studies describing the development process or the psychometric characteristics of RAI-HC QIs were eligible. The data extraction involved a general description of the included studies as well as the identified RAI-HC QIs and information on validity and reliability. The methodological quality of the identified RAI-HC QI sets was assessed using the Appraisal of Indicators through Research and Evaluation (AIRE) instrument.
Four studies out of 659 initial hits met the inclusion criteria. The included studies described the development and validation process of three RAI-HC QI sets comprising 48 unique RAI-HC QIs, which predominantly refer to outcome of care. Overall, the validity and reliability of the identified RAI-HC QIs were not sufficiently tested. The methodological quality of the three identified RAI-HC QI sets varied across the four AIRE instrument domains. None of the QI sets reached high methodological quality, defined as scores of 50% and higher in all four AIRE instrument domains.
This is the first review that systematically summarized and appraised the available scientific evidence on the validity and reliability of RAI-HC QIs. It identified insufficient reporting of RAI-HC QIs validation processes and reliability as well as missing state-of-the-art methodologies. The review provides guidance as to what additional validity and reliability testing are needed to strengthen the scientific soundness of RAI-HC QIs. Considering that RAI-HC QIs are already implemented and used to measure and compare quality of home care, further investigations on RAI-HC QIs reliability and validity is recommended.
The change of populations’ age-structure has a significant impact on health systems worldwide and, in particular, poses challenges for home care . Home care in the context of this study is defined as medical and personal care provided by professional nursing staff within clients’ own homes. Globally, the number of people aged 60 and older is expected to double by 2050 . As larger demographic cohorts enter old age and life expectancy increases, more people will live with chronic illnesses, multi-morbidity, as well as functional and cognitive impairments . Findings have shown that the large majority of older people in need of care prefer to remain in their known physical and social environment for as long as possible, leading to increased demand for home care . In order to satisfy peoples’ preferences and to reduce costs of long-term institutional care, many countries have promoted home care in recent decades by shifting resources accordingly . Given the growing importance of home care, it is essential to assess and monitor the quality of the delivered care.
Quality indicators (QIs) are increasingly used to measure, monitor and evaluate health care quality by assessing particular structures, processes, or outcomes. QIs can point to areas where the quality of care is suboptimal, subsequently allowing priorities to be set for quality improvement . Moreover, QIs are used for comparisons of health care quality and thus enable national or international benchmarking . Similar to other measuring instrument, QIs should meet certain quality criteria such as relevance and feasibility and be evaluated for their scientific strength, i.e. their validity and reliability . QIs that do not meet these quality criteria can result in inadequate and misleading information.
In many countries in North America, Europe and Asia-Pacific, quality of home care is assessed with QIs based on the Resident Assessment Instrument-Home Care (RAI-HC or interRAI-HC). RAI-HC was developed in 1994 by the multinational research consortium interRAI. The instrument is a standardized assessment tool to assess long-stay home care clients’ health status, need for care, and basic background information on housing and informal caregivers. The instrument RAI-HC [8,9,10,11,12,13] and its clinically based scales (e.g. Cognitive Performance Scale, Depression Rating Scale) [14,15,16,17,18,19,20] have been validated in several international studies. Although the principal intended use of RAI-HC is to plan care provision, RAI-HC items and scales are also used to derive process and outcome QIs . These RAI-HC QIs are rate-based indicators, i.e. defined by a numerator and denominator, and measure processes or outcomes expected to occur with a certain frequency .
To date, no systematic review has been undertaken to summarize the scientific soundness, such as the validity and reliability of RAI-HC QIs, despite the fact that these indicators are implemented in several countries and applied by researchers to measure and compare the quality of home care [23,24,25,26]. The only previous overview and quality assessment of QIs for community care by Joling et al.  included RAI-HC QIs only partially, as it focused on QIs specifically developed for older people or applied in an older aged sample (i.e. 65 years or older). Therefore, this systematic review aimed to (i) identify all current existing RAI-HC QIs and to (ii) summarize the scientific evidence of RAI-HC QIs validity and reliability.
The systematic review was conducted in compliance with The Cochrane Handbook for Systematic Reviews of Interventions . The protocol for the systematic review has been published on PROSPERO (2018: CRD42018110948) and is available at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=110948.
The search was carried out using five electronic databases: PubMed, CINAHL, Embase, PsycINFO and Cochrane Library on June 26, 2018. An update of the search was conducted on August 20, 2019, resulting in no additional eligible articles. The search strategy involved both keywords and the Medical Subject Headings (MeSH) combined with the appropriate Boolean connectors (see Additional file 1). In addition, the reference lists of the eligible studies were manually searched for additional relevant articles that had not been identified in the electronic database. Furthermore, we searched for grey literature on websites of relevant organizations (e.g. www.interrai.org) and contacted the study authors of two included articles, namely Burla et al.  and Morris et al. , to obtain additional information on the QIs definitions used in their study.
Inclusion and exclusion criteria
Studies were included if they fulfilled the following criteria: (i) the study was conducted in the home care setting, (ii) included adults aged 18 years and older, and (iii) described the development process of RAI-HC QIs or evaluated the psychometric characteristics of RAI-HC QIs.
Studies were excluded if they (i) used RAI-HC or its scales without explicitly using the RAI-HC QIs, (ii) applied already developed RAI-HC QIs for quality measure, (iii) validated the RAI-HC or its scales but not RAI-HC QIs, and (iv) focused on specialized home care for mental health or palliative care. We excluded studies focusing on mental health and palliative care because the needs of these home care clients are different from those of general long-stay home care clients. Therefore, specialized assessment instruments such as the interRAI Palliative Care (PC) and the interRAI Community Mental Health (CMH) are available to assess the needs of mental health and palliative care clients and to plan their care provision. Only recently, QIs have been developed to measure the quality in these contexts [31, 32].
The studies identified by the electronic search were entered into the reference management software Endnote X8, and duplicates were removed. The articles were independently screened by two authors (AW and FZ) according to the inclusion criteria first by title and abstract. Non-eligible studies were removed at this stage. Publications included after the title/abstract screening underwent concurrent full-text screening by two reviewers (AW and FZ) for definitive inclusion. Any disagreements that arose between the reviewers were resolved through discussion until consensus was reached.
Two data extraction forms were developed. First, a structured form was used to describe the included studies with respect to relevant information regarding RAI-HC QIs. The following data were extracted: first author, year of publication, country, study aim, study population, sample size, name of QI set, the number of QIs in the set and a short description of the development and validation process of the QI set. Second, a structured form was used to extract and summarize the identified RAI-HC QIs. For each QI, the following data were extracted: QI description such as name, type (prevalence or incidence), name of the corresponding QI set, and data regarding validity and reliability. Furthermore, the QIs were classified by the study authors (AW and FZ) according to their measure level (outcome or process). Missing data regarding QI definitions were requested from study authors.
We used the Appraisal of Indicators through Research and Evaluation (AIRE) instrument for the methodological assessment of the RAI-HC QIs identified in the articles . AIRE is a validated instrument for a critical appraisal of QIs and has been used in previous scientific publications on QIs [27, 34,35,36]. The AIRE instrument comprises 20 items, subdivided into four domains: 1. Purpose, relevance and organizational context, 2. Stakeholder involvement, 3. Scientific evidence, and 4. Additional evidence, formulation and usage. Two authors (AW and NM) independently appraised all included QI sets with the AIRE instrument. Information from the included articles as well as QI definitions received from study authors on request were used for the assessment. Standardized scores per domain were calculated. Scores range between 0 and 100%, with a higher score indicating a higher methodological level. A high methodological quality is defined by a score of 50% or higher, which correlates with an overall “agree” or “strongly agree” for each domain.
The systematic review identified 659 potentially relevant studies. The PRISMA flow diagram for the study selection process and reasons for exclusion are shown in Fig. 1. After removal of duplicates and title/abstract screening, 21 studies were potentially relevant. Four studies met the selection criteria after full-text screening [29, 30, 37, 38]. Reference tracking of the eligible studies identified no additional article. The four included studies describe three RAI-HC QI sets comprising 48 unique RAI-HC QIs.
Description of studies
Table 1 shows the main characteristics of the selected papers. Three studies specified the development and validation process of separate RAI-HC QI sets, namely, interRAI’s 1st generation QI set developed by Hirdes et al. , the Swiss RAI-HC QI set developed by Burla et al. , and interRAI’s 2nd generation QI set developed by Morris et al. . One study by Dalby et al.  explored the effects of risk adjustment for interRAI’s 1st generation QI set. The included studies were not primarily aimed at examining the validity and reliability of RAI-HC QIs, and only partially reported such results.
Development and validation process of quality indicator sets
InterRAI’s 1st generation quality indicator set
Hirdes et al.  described the development and validation process of interRAI’s 1st generation QI set. Initially, a literature review was conducted to identify candidate QIs used in other care settings. In addition, expert meetings and focus groups with health professionals and older adults were conducted to identify further candidate QIs. The literature review (e.g. search string, results, synthesis) and process of the focus groups were not described in the article. In total, 74 candidate QIs were generated which were first prioritized by the investigators according to their relevance to different types of home care clients and second, ranked in terms of their appropriateness for the different types of home care clients. The average ranks were then used to reduce the number of candidate QIs. The article offers no information on the ranking process, the average ranks and the criteria determining when a QI was considered inappropriate. The QIs were empirically tested with regard to relative frequencies, variation and denominator size based on data from 14,293 home care clients from Ontario and Michigan. QIs with a relative frequency of less than 5% and more than 95%, respectively, and too little variation (interquartile range) among HCOs were excluded. The article provides no information on the basis of which criteria QI variation was considered as insufficient. Based on the overall study results, a final set of 22 QIs was defined, for which risk adjustment methods such as client-level covariates and an agency-level adjuster, namely, the Agency Intake Profile (AIP), has been quantitatively evaluated .
Dalby et al.  further explored the effects of risk adjustment for interRAI’s 1st generation QI set. Based on data of 22 HCOs in Ontario and the Winnipeg Regional Health Authority (WRHA) in Manitoba, three types of risk adjustment methods were applied, namely, client covariates only, client covariates plus AIP, and client covariates plus the intake Case Mix Index associated with the Resource Utilization Groups version III for Home Care methodology . Based on the three approaches, risk adjustment showed substantial effects on the organization level but only small effects on the regional level. On the regional level, the risk adjustment process minimized the differences in QI rates between Ontario and the WRHA compared with the unadjusted rates. On the organization level, risk adjustment had an impact on agency rankings across the set of QIs. While the HCOs in Ontario benefited from the risk adjustment, i.e. they were less likely to be ranked among the worst performers, the opposite was true for the HCOs in the WRHA .
The Swiss RAI-HC quality indicator set
The development and validation process of the Swiss RAI-HC QI set was described by Burla et al. . Based on interRAI’s 1st generation QI set and by creating new QIs for the Swiss context with support of various experts, 29 candidate QIs were chosen. The QIs were rated according to their appropriateness of measuring home care quality in focus groups with health care professionals from HCOs using the nominal group technique (NGT). The rating process and the QI ratings were presented in the paper. Relative frequencies of all candidate QIs and the variation of 24 QIs (due to small sample size) were examined based on data from 1808 home care clients from Switzerland. QIs with a relative frequency of less than 5% or more than 95% and/or a low variation (interquartile difference less than 6%) were specified as inadequate. Furthermore, due to small sample size interrater reliability was analyzed in only 18 QIs. For this purpose, 24 home care clients were independently assessed by two assessors. The results of the expert rating, frequencies, variation, and interrater reliability were summarized and only QIs that met at least three of the four criteria were defined as appropriate, resulting in a final core-set of 19 QIs .
InterRAI’s 2nd generation quality indicator set
Morris et al.  described the development and validation process of interRAI’s 2nd generation QI set. A list of 64 candidate QIs was compiled including both 1st generation QIs as well as newly designed QIs, drawn from lists of QIs that interRAI has considered for home care, post-acute care, and long-term care. The QIs were empirically tested with regard to relative frequencies and variation based on a sample of 335,544 home care clients from the U.S., Canada and Europe. QIs with a relative frequency of less than 3% were excluded. Variation was examined by comparing scores of top performing HCOs (5th percentile) with scores of the poorest performing HCOs (95th percentile). QIs with less than a two-fold difference in scores from the 5th to 95th percentile, thus not discriminatory enough, were dropped. Additionally, factor analysis was conducted for eight functional QIs measuring improvement and decline in cognition, communication, activities of daily living (ADL) and instrumental activities of daily living (IADL) to confirm that functional decline and improvement QIs say something different about the performance of HCOs. The QIs were further evaluated by representatives of HCOs in focus groups and one-on-one discussions to determine if the QIs could be influenced by efforts of HCOs. The remaining QIs were reviewed regarding face validity by 16 members of interRAI’s cross-national program development committee. Each QI had to be approved by at least 70% of the members to remain on the QI list. Rating results, rating criteria and the exact method of consensus are not indicated in the article. The definite QI set comprised 23 QIs. For the QI set, a new approach of risk adjustment was employed with more complex covariate structures, a longer list of covariates, and direct stratification .
Validity and reliability of RAI-HC quality indicators
Table 2 gives an overview of the characteristics of the 48 identified RAI-HC QIs and summarizes findings on their validity and reliability. Face validity was examined for all identified QIs based on expert opinion. However, only Burla et al.  described rating results for 29 Swiss RAI-HC QIs, with seven QIs rated as inappropriate to measure quality of home care. Reliability was addressed explicitly by Burla et al.  providing results of interrater reliability testing for the RAI-HC items used for the calculation of 18 Swiss RAI-HC QIs. Interrater reliability was calculated by Burla et al.  using Kappa and Yules. The higher Kappa/Yules (range 0–1), the higher the agreement between the two independent assessors. Twelve QIs showed moderate (Kappa/Yules values 0.40–0.59) or good interrater reliability (Kappa/Yules values ≥0.60) and six QIs had insufficient interrater reliability (Kappa/Yules values < 0.40) [29, 40].
Methodological characteristics of RAI-HC quality indicator sets
The methodological quality of the three identified RAI-HC QI sets varied according to the AIRE instrument domain scores (see Table 3). The AIRE instrument domain ratings ranged from 0 to 69%. None of the QI sets reached high methodological quality, defined as scores of 50% or higher in all four AIRE instrument domains.
InterRAI’s 1st generation QI set  and the Swiss RAI-HC QI set  scored 50% or higher in the first AIRE instrument domain demonstrating good evidence for “purpose, relevance and organizational context”. InterRAI’s 1st generation QI set  and the Swiss RAI-HC QI set  scored poorly in the domain “Stakeholder involvement” due to a lack of involvement of relevant stakeholders at some stage of the development process. The three QI sets scored between 0 and 11% in the domain “Scientific evidence”. None of the three studies performed a systematic review to investigate evidence-based guidelines supporting QIs nor did they examine the relationships between care processes and outcomes. The domain “Additional evidence, formulation and usage” indicated for interRAI’s 1st and 2nd generation QI set [30, 37] a good methodological quality with scores of 50% or higher.
In this systematic review, the three identified RAI-HC QI sets contained a total of 48 unique QIs covering various health domains and predominantly referring to outcome of care. To be able to draw valid conclusions from QIs, it is relevant to establish the validity and reliability of the QIs. The methodological assessment of the three QI sets, however, indicated relatively low methodological quality and a lack of evidence of validity and reliability.
QI’s validity, such as face, content, construct, and criterion aspects, should either be supported by scientific literature or be examined. When addressing scientific evidence, it is recommended to follow a systematic approach and to search both for scientific as well as grey literature, and not only to identify articles regarding the validity of QIs but also articles that discuss the outcome of interest . However, this review showed that the above recommendations were not applied in the development processes of extant RAI-HC QIs. The majority of identified RAI-HC QIs were adopted from other care settings or other interRAI QI sets, and in none of the included studies was a systematic review carried out to identify candidate QIs or to address scientific evidence on QIs.
In practice, and in the absence of scientific literature on QIs, face validity of QIs is often assessed based on the opinions and experience of experts . Commonly used structured consensus techniques for QI development combining expert opinion with available evidence are the Delphi techniques , the RAND/UCLA Appropriateness Method  and the NGT . The advantages of these approaches are that experts can be included anonymously and the undue social influence processes toward expert consensus can be minimized . In all three identified RAI-HC QI sets, experts were involved to evaluate the face validity of QIs, however, such a structured approach was only applied by Burla et al.  using the NGT in the development process of the Swiss RAI-HC QIs. Hirdes et al.  did not report on the exact process of assessing face validity for interRAI’s 1st generation QIs and the consensus process used by Morris et al.  for interRAI’s 2nd generation QIs was incompletely described. Considering that consensus for both interRAI QI sets was reached in an unstructured way and by face-to-face discussion only, rating results could be biased toward the opinions of influential or persuasive experts.
Other forms of validity such as content, construct and criterion validity of RAI-HC QIs have not been examined. Admittedly, similar to nursing home QIs [46, 47], criterion validity of home care QIs can be difficult to measure because few, if any, valid “gold standard” measurements exist that can be used for comparison. While some of the identified RAI-HC QIs are calculated using validated RAI-HC scales (e.g. Cognitive Performance Scale, Depression Rating Scale and Pain Scale) [14, 16,17,18], the use of validated scales does not necessarily ensure the validity of the corresponding QI. RAI-HC scales have not been developed to evaluate quality of care. In the absence of QI “gold standard” measurements, construct validity of QIs is often examined instead of criterion validity, e.g. by assessing correlations between process and outcome QIs [36, 48, 49]. However, such assessments were not carried out in the reviewed articles since there are only few process RAI-HC QIs.
The review revealed that the reliability of the identified RAI-HC QIs has hardly been tested so far. For example, interrater reliability of RAI-HC QIs, respectively the underlying RAI-HC items, was only assessed by Burla et al.  for a subset of Swiss RAI-HC QIs. Burla et al.  found moderate or good interrater reliability for 12 QIs and insufficient interrater reliability for six QIs. Possible reasons for poor interrater reliability are difficulty to detect certain health conditions (e.g. delirium), a high rate of true clinical change and the fluctuations of symptoms and function, misinterpretation of assessment instructions, or poorly designed assessment items . Further, Burla et al.  point out that the interrater reliability findings of the 18 QIs must be interpreted cautiously due to the small sample size (only 24 home care clients were assessed). In contrast to the RAI-HC QIs, the psychometric properties of the instrument RAI-HC, i.e. its items and scales, has been tested more thoroughly. These validation studies show overall acceptable reliability results [8, 10, 11, 13]. Nevertheless, good reliability of RAI-HC data does not guarantee the reliability of QIs based on these data nor the ability to be applied in quality comparisons over time or between providers [51, 52].
In addition, the reported validation process of RAI-HC QIs in the included articles did not involve precision tests to determine the reliability of QIs for distinguishing real differences in performance. In the reviewed studies, between-provider variation was assessed by comparing QI rates (e.g. interquartile range) among HCOs and geographic regions, whereby for interRAI’s 1st and 2nd generation QIs unadjusted as well as risk-adjusted rates and for the Swiss RAI-HC QIs only unadjusted rates were computed. However, the possibility that a substantial amount of between-provider variation is attributable to random variation has not been considered. Chance can cause substantial differences in performance in the absence of true quality differences . The empirical evaluation of QIs for the acute care [6, 53,54,55] and nursing home setting [46, 56,57,58,59] includes other statistical methods to test for reliability such as the intra-class correlation coefficient or more advanced modeling techniques, such as multilevel or Bayesian-based estimation as well as thorough risk adjustments. To determine whether RAI-HC QIs have the ability to consistently measure quality differences in home care, the application of the above mentioned statistical procedures should be considered.
Strengths and limitations
To the authors’ knowledge, this is the first attempt to identify and summarize, in a systematic way, the scientific evidence on validity and reliability of RAI-HC QIs, thereby identifying gaps for potential improvement in future validation studies. The review was limited by the small number of articles available. While it cannot be ruled out that validation studies regarding RAI-HC QIs may not have been published in peer-reviewed journals, grey literature searches did not provide additional publications. To the best of our knowledge, we have reviewed all published work on the validity and reliability of RAI-HC QIs. Due to the poor reporting of methodology and results, it is difficult to draw a firm conclusion on the overall validity and reliability of the QIs. Furthermore, the QI assessment with the AIRE instrument was hindered by the limited information in the validation processes of the RAI-HC QI sets. Thus, the AIRE instrument rating results have to be interpreted cautiously.
Based on the description of the RAI-HC QI sets, the validation processes, and the methodological assessment with the AIRE instrument, this review indicates that the quality of the evidence underpinning the identified RAI-HC QIs is weak and information about validity and reliability is scarce. QIs that are not valid and reliable result in an inaccurate or unreliable measure of the quality of care and, therefore, are neither useful for identifying poor nor desirable quality of care . In addition, information on the methodological quality of QIs is crucial for different stakeholders such as health care providers or policy-makers when selecting QIs for their intended use. This review provides suggestions as to what additional testing of QIs are needed to strengthen their scientific soundness. Considering that RAI-HC QIs are already implemented and frequently used by HCOs for quality improvement processes but also in scientific research to measure and compare home care quality among HCOs or regions, more evidence of the validity and reliability of RAI-HC QIs is essential.
Availability of data and materials
Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.
Activities of daily living
Agency Intake Profile
Appraisal of Indicators through Research and Evaluation
Home care organization
Instrumental activities of daily living
Nominal group technique
Resident Assessment Instrument-Home Care
the Winnipeg Regional Health Authority
Colombo F, Llena-Nozal A, Mercier J, Tjadens F. Help wanted? Providing and paying for long-term care. OECD Publishing; 2011. doi:10.1787/9789264097759-en.
United Nations, Department of Economics and Social Affairs, population division. World population ageing 2017. 2017. https://www.un.org/en/development/desa/population/theme/ageing/WPA2017.asp. Accessed 4 Sep 2019.
Lafortune G, Balestat G. Trends in severe disability among elderly people: assessing the evidence in 12 OECD countries and the future implications. OECD health working papers. Paris: OECD; 2007.
Lehnert T, Heuchert M, Hussain K, König HH. Stated preferences for long-term care: a literature review. Ageing Soc 2019;39:1873–1913. doi:10.1017/S0144686X18000314.
Mainz J. Defining and classifying clinical indicators for quality improvement. Int J Qual Health Care 2003;15:523–530. doi:10.1093/intqhc/mzg081.
Lovaglio PG. Benchmarking strategies for measuring the quality of healthcare: problems and prospects. Sci World J 2012;2012:606154. doi:10.1100/2012/606154.
Campbell S, Braspenning J, Hutchinson A, Marshall M. Research methods used in developing and applying quality indicators in primary care. Qual Saf Health Care 2002;11:358–364. doi:10.1136/qhc.11.4.358.
Morris JN, Fries BE, Steel K, Ikegami N, Bernabei R, Carpenter GI, et al. Comprehensive clinical assessment in community setting: applicability of the MDS-HC. J Am Geriatr Soc 1997;45:1017–1024. doi:10.1111/j.1532-5415.1997.tb02975.x.
Landi F, Tua E, Onder G, Carrara B, Sgadari A, Rinaldi C, et al. Minimum data set for home care: a valid instrument to assess frail older people living in the community. Med Care 2000;38:1184–1190. doi:10.1097/00005650-200012000-00005.
Hirdes JP, Ljunggren G, Morris JN, Frijters DH, Finne Soveri H, Gray L, et al. Reliability of the interRAI suite of assessment instruments: a 12-country study of an integrated health information system. BMC Health Serv Res 2008;8:277. doi:10.1186/1472-6963-8-277.
Poss JW, Jutan NM, Hirdes JP, Fries BE, Morris JN, Teare GF, et al. A review of evidence on the reliability and validity of minimum data set data. Healthc Manage Forum 2008;21:33–39. doi:10.1016/S0840-4704(10)60127-5.
Kim H, Jung YI, Sung M, Lee JY, Yoon JY, Yoon JL. Reliability of the interRAI long term care facilities (LTCF) and interRAI home care (HC). Geriatr Gerontol Int 2015;15:220–228. doi:10.1111/ggi.12330.
Hogeveen SE, Chen J, Hirdes JP. Evaluation of data quality of interRAI assessments in home and community care. BMC Med Inform Decis Mak 2017;17:150. doi:10.1186/s12911-017-0547-9.
Morris JN, Fries BE, Mehr DR, Hawes C, Phillips C, Mor V, et al. MDS cognitive performance scale. J Gerontol 1994;49:M174–M182. doi:10.1093/geronj/49.4.M174.
Hartmaier SL, Sloane PD, Guess HA, Koch GG, Mitchell CM, Phillips CD. Validation of the minimum data set cognitive performance scale: agreement with the mini-mental state examination. J Gerontol Ser A. 1995;50A:M128–M133. doi:10.1093/gerona/50A.2.M128.
Morris JN, Fries BE, Morris SA. Scaling ADLs within the MDS. J Gerontol Ser A. 1999;54:M546–M553. doi:10.1093/gerona/54.11.M546.
Burrows A. Development of a minimum data set-based depression rating scale for use in nursing homes. Age Ageing 2000;29:165–172. doi:10.1093/ageing/29.2.165.
Fries BE, Simon SE, Morris JN, Flodstrom C, Bookstein FL. Pain in U.S. nursing homes: validating a pain scale for the minimum data set. The Gerontologist 2001;41:173–179. doi:10.1093/geront/41.2.173.
Leung DY, Leung AY, Chi I. An evaluation of the factor structure of the instrumental activities of daily living involvement and capacity scales of the minimum data set for home care for elderly chinese community dwellers in Hong Kong. Home Health Care Serv Q 2011;30:147–159. doi:10.1080/01621424.2011.592421.
Penny K, Barron A, Higgins AM, Gee S, Croucher M, Cheung G. Convergent validity, concurrent validity, and diagnostic accuracy of the interRAI depression rating scale. J Geriatr Psychiatry Neurol 2016;29:361–368. doi:10.1177/0891988716666376.
Salahudeen MS, Nishtala PS. A systematic review evaluating the use of the interRAI home care instrument in research for older people. Clin Gerontol 2019;42:463–484. doi:10.1080/07317115.2018.1447525.
Quality indicators. interRAI. http://www.interrai.org/indicators.html. Accessed 1 Sep 2019.
Bos JT, Frijters DHM, Wagner C, Carpenter GI, Finne-Soveri H, Topinkova E, et al. Variations in quality of home care between sites across Europe, as measured by home care quality indicators. Aging Clin Exp Res 2007;19:323–329. doi:10.1007/BF03324709.
Mofina AM, Guthrie DM. A comparison of home care quality indicator rates in two Canadian provinces. BMC Health Serv Res 2014;14:37. doi:10.1186/1472-6963-14-37.
Lo AT, Gruneir A, Bronskill SE, Bierman AS. Sex differences in home care performance: a population-based study. Womens Health Issues 2015;25:232–238. doi:10.1016/j.whi.2015.01.004.
Foebel AD, van Hout HP, van der Roest HG, Topinkova E, Garms-Homolova V, Frijters D, et al. Quality of care in European home care programs using the second generation interRAI home care quality indicators (HCQIs). BMC Geriatr 2015;15. doi:10.1186/s12877-015-0146-5.
Joling KJ, van Eenoo L, Vetrano DL, Smaardijk VR, Declercq A, Onder G, et al. Quality indicators for community care for older people: a systematic review. PLoS One 2018;13:e0190298. doi:10.1371/journal.pone.0190298.
Higgins JPT, Green S. Cochrane handbook for systematic reviews of interventions. Chichester, England; Hoboken, NJ: Wiley; 2008.
Burla L, Schaffert R, Mylaeus M, Rüesch P. [Developing and testing quality indicators of home care in Switzerland]. Gesundheitswesen. 2010;72:106–113. doi:10.1055/s-0029-1233474.
Morris JN, Fries BE, Frijters D, Hirdes JP, Steel RK interRAI home care quality indicators. BMC Geriatr 2013;13:127. doi:10.1186/1471-2318-13-127.
Harman LE, Guthrie DM, Cohen J, Declercq A, Fisher K, Goodridge D, et al. Potential quality indicators for seriously ill home care clients: a cross-sectional analysis using resident assessment instrument for home care (RAI-HC) data for Ontario. BMC Palliat Care 2019;18:3. doi:10.1186/s12904-018-0389-y.
Seow H, Harman L, Barbera L, Sutradhar R, McGrail K, Burge F, et al. Pan-Canadian quality indicators for patients at end of life derived from interRAI data. J Pain Symptom Manag 2018;56:e59–e60. doi:10.1016/j.jpainsymman.2018.10.174.
de Koning J, Smulders A, Klazinga N. The appraisal of indicators through research and evaluation (AIRE) instrument. Amsterdam: Academic Medical Center; 2006.
Burkett E, Martin-Khan MG, Gray LC. Quality indicators in the care of older persons in the emergency department: a systematic review of the literature. Australas J Ageing 2017;36:286–298. doi:10.1111/ajag.12451.
Petrosyan Y, Sahakyan Y, Barnsley JM, Kuluski K, Liu B, Wodchis WP. Quality indicators for care of depression in primary care settings: a systematic review. Syst Rev 2017;6:126. doi:10.1186/s13643-017-0530-7.
Voeten SC, Krijnen P, Voeten DM, Hegeman JH, Wouters MWJM, Schipper IB. Quality indicators for hip fracture care, a systematic review. Osteoporos Int 2018;29:1963–1985. doi:10.1007/s00198-018-4558-x.
Hirdes JP, Fries BE, Morris JN, Ikegami N, Zimmerman D, Dalby DM, et al. Home care quality indicators (HCQIs) based on the MDS-HC. The Gerontologist 2004;44:665–679. doi:10.1093/geront/44.5.665.
Dalby DM, Hirdes JP, Fries BE. Risk adjustment methods for home care quality indicators (HCQIs) based on the minimum data set for home care. BMC Health Serv Res 2005;5:7. doi:10.1186/1472-6963-5-7.
Kidder D, Rennison M, Goldberg H, Warner D, Bell B, Hadden L, et al. MegaQI covariate analysis and recommendations: identification and evaluation of existing quality indicators that are appropriate for use in long-term care settings. Cambridge, Massachusetts: Abt Associates Inc; 2002.
Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 1994;6:284–290. doi:10.1037/1040-35220.127.116.114.
Center for Health Policy/Center for Primary Care and Outcomes Research & Battelle Memorial Institute. Quality indicator measure development, implementation, maintenance, and retirement. Rockville, MD: Agency for Healthcare Research and Quality; 2011. https://www.qualityindicators.ahrq.gov/Downloads/Resources/Publications/2011/QI_Measure_Development_Implementation_Maintenance_Retirement_Full_5-3-11.pdf.
Boulkedid R, Abdoul H, Loustau M, Sibony O, Alberti C. Using and reporting the Delphi method for selecting healthcare quality indicators: a systematic review. PLoS One 2011;6:e20476. doi:10.1371/journal.pone.0020476.
Linstone HA, Turoff M. The Delphi method: techniques and applications. London: Addison-Wesley; 1975.
Fitch K, Bernstein SJ, Aguilar MD, Burnand B, LaCalle JR, Lazaro P, et al. The RAND/UCLA appropriateness method user’s manual. Santa Monica: RAND Corporation; 2001.
Delbecq AL, van de Ven AH, Gustafson DH. Group techniques for program planning: a guide to nominal group and delphi processes. Glenview: Scott, Foresman & Co.; 1975.
Jones RN, Hirdes JP, Poss JW, Kelly M, Berg K, Fries BE, et al. Adjustment of nursing home quality indicators. BMC Health Serv Res 2010;10:96. doi:10.1186/1472-6963-10-96.
Nakrem S, Vinsnes AG, Harkless GE, Paulsen B, Seim A. Nursing sensitive quality indicators for nursing home care: international review of literature, policy and practice. Int J Nurs Stud 2009;46:848–857. doi:10.1016/j.ijnurstu.2008.11.005.
Fischer C, Lingsma HF, Anema HA, Kievit J, Steyerberg EW, Klazinga N. Testing the construct validity of hospital care quality indicators: a case study on hip replacement. BMC Health Serv Res 2016;16. doi:10.1186/s12913-016-1778-7.
Gooiker GA, Kolfschoten NE, Bastiaannet E, Velde CJH van de, Eddes EH, Harst E van der, et al. Evaluating the validity of quality indicators for colorectal cancer care. J Surg Oncol 2013;108:465–471. doi:10.1002/jso.23420.
Hirdes JP, Smith TF, Rabinowitz T, Yamauchi K, Pérez E, Telegdi NC, et al. The resident assessment instrument-mental health (RAI-MH): inter-rater reliability and convergent validity. J Behav Health Serv Res 2002;29:419–432. doi:10.1007/BF02287348.
Berg K, Mor V, Morris J, Murphy KM, Moore T, Harris Y. Identification and evaluation of existing nursing homes quality indicators. Health Care Financ Rev. 2002;23:19–36.
Hutchinson AM, Milke DL, Maisey S, Johnson C, Squires JE, Teare G, et al. The resident assessment instrument-minimum data set 2.0 quality indicators: a systematic review. BMC Health Serv Res 2010;10:166. doi:10.1186/1472-6963-10-166.
Fung V, Schmittdiel JA, Fireman B, Meer A, Thomas S, Smider N, et al. Meaningful variation in performance: a systematic literature review. Med Care 2010;48:140–148. doi:10.1097/MLR.0b013e3181bd4dc3.
Selby JV, Schmittdiel JA, Lee J, Fung V, Thomas S, Smider N, et al. Meaningful variation in performance: what does variation in quality tell us about improving quality? Med Care 2010;48:133–139. doi:10.1097/MLR.0b013e3181c15a6e.
van Dishoeck AM, Lingsma HF, Mackenbach JP, Steyerberg EW. Random variation and rankability of hospitals using outcome indicators. BMJ Qual Saf 2011;20:869–874. doi:10.1136/bmjqs.2010.048058.
Berlowitz DR, Christiansen CL, Brandeis GH, Ash AS, Kader B, Morris JN, et al. Profiling nursing homes using bayesian hierarchical modeling. J Am Geriatr Soc 2002;50:1126–1130. doi:10.1046/j.1532-5415.2002.50272.x.
Castle NG, Degenholtz H, Engberg J. State variability in indicators of quality of care in nursing facilities. J Gerontol Ser A 2005;60:1173–1179. doi:10.1093/gerona/60.9.1173.
Arling G, Kane RL, Lewis T, Mueller C. Future development of nursing home quality indicators. The Gerontologist 2005;45:147–156. doi:10.1093/geront/45.2.147.
Arling G, Lewis T, Kane RL, Mueller C, Flood S. Improving quality assessment through multilevel modeling: the case of nursing home compare. Health Serv Res 2007;42:1177–1199. doi:10.1111/j.1475-6773.2006.00647.x.
We wish to thank Cornelis Kooijman and Esther Baettig from the Swiss Association of Home Care Organizations (Sitex Schweiz) for their support. The study relates to ongoing work by the HCD (HomeCareData) Research Group, who provided input on the results.
The HCD Research Group consists of:
Institute of Health Sciences, ZHAW: Julia Dratva, René Schaffert, Aylin Wagner.
Winterthur Institute of Health Economics, ZHAW: Eva Hollenstein, Florian Liberatore, Sarah Schmelzer.
Swiss Health Observatory (OBSAN): Laure Dutoit, Sonia Pellegrini.
Institute of Social and Preventive Medicine, University of Bern: Adrian Spoerri, Andreas Boss.
Ethics approval and consent to participation
The study was funded by the Swiss National Science Foundation (SNSF), National Research Program 74 “Smarter Health Care”, Project “Swiss Home Care Data: patient profiles and quality measures for home care” (No. 167499); and the Swiss Federal Office of Public Health (FOPH). The funding bodies had no role in the design nor the execution of the study. Further, they did not participate in analysis or interpretation of the data, nor manuscript writing.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Wagner, A., Schaffert, R., Möckli, N. et al. Home care quality indicators based on the Resident Assessment Instrument-Home Care (RAI-HC): a systematic review. BMC Health Serv Res 20, 366 (2020). https://doi.org/10.1186/s12913-020-05238-x
- Systematic review
- Quality indicators
- Home care
- Home-based care