Abir Abdellatif

MSc Jacques Bouaud

PhD Carmelo Lafuente-Lafuente

PhD Joël Belmin
email: j.belmin@aphp.fr

PhD Brigitte Séroussi

A complete scoping review of computerized decision support systems designed for nursing homes

Objectives:

To summarize the research literature describing the outcomes of computerized decision support systems (CDSSs) implemented in nursing homes (NHs).

Design: Scoping review

Methods: Search of relevant articles published in English language between January 1, 2000, and February 29, 2020 in the Medline database. The quality of the selected studies was assessed according to PRISMA guidelines and the Mixed Method Appraisal Tool (MMAT).

Results: From 1828 articles retrieved, 24 studies were selected for review, among which only six were randomized controlled trials. Although clinical outcomes are seldom studied, some studies show that CDSSs have the potential to decrease pressure ulcer incidence and malnutrition prevalence. Improvement of process outcomes such as increased compliance with practice guidelines, better documentation of nursing assessment, improved teamwork and communication, and cost saving, are also reported.

Conclusions and implications:

Overall, the use of CDSSs in NHs may be effective to improve patient clinical outcomes and healthcare delivery. However, most of the retrieved studies were observational studies, which significantly weakens the evidence. High quality studies are needed to investigate CDSS effects and limitations in NHs.

Funding sources:

• This research was supported by the French National Association for Research in Technology (ANRT) -CIFRE-Industrial Agreements for Research Training under (Grant n° 2018/0307) for Abir ABDELLATIF.

Word, references and graphics count:

• Word count: main text: 4513 ; abstract: 178.

• Reference count : 54

• Graphics count : 4 Tables, 1 Figure

• Supplemental material: 2 Tables Brief summary : We reviewed 24 articles analyzing computerized decision support systems (CDSSs) used in nursing homes. CDSS use was associated with improved process/health outcomes in several studies. It might contribute to the quality of care and the health of residents.

Acknowledgments: none.

Sponsor's role in the design, methods, data collection, analysis and preparation of the paper: none.

INTRODUCTION

With aging, people are suffering from multiple chronic diseases leading to a high prevalence of dependency, and a high number of older adults living in nursing homes (NHs) despite the improvement of care at home. [START_REF] Kojima | Frailty as a predictor of nursing home placement among community-dwelling older adults: a systematic review and meta-analysis[END_REF][START_REF] De La Rica-Escuín | Frailty and mortality or incident disability in institutionalized older adults: the FINAL study[END_REF] Because they are suffering from multiple chronic diseases, older adults are subject to polypharmacy which put them at risk of taking potentially inappropriate drugs and experiencing adverse drug events (ADEs). Thus, NH residents are a highly vulnerable population with severe medical issues that require complex management with a high level of geriatric and nursing expertise. [START_REF] Goodman | Effective health care for older people living and dying in care homes: a realist review[END_REF] However, nurses' expertise is often lacking due to insufficient knowledge and limited staff training efforts because of high staff turnover. Missed nursing care is common in NHs due to inadequate time or resources. [START_REF] White | Registered nurse burnout, job dissatisfaction, and missed care in nursing homes[END_REF] Besides, health professionals are involved in high regulatory requirement that may conflict with or distract from clinical practice guideline implementation. [START_REF] Resnick | Testing the Feasibility of Implementation of Clinical Practice Guidelines in Long-Term Care Facilities[END_REF] Thus, studies have reported that NH residents may experience several potentially preventable clinical complications, such as pressure ulcers, falls, malnutrition, or ADEs. [START_REF] Clegg | Frailty in Older People[END_REF][START_REF] Gurwitz | Incidence and preventability of adverse drug events among older persons in the ambulatory setting[END_REF][START_REF] Thomas | Incidence and types of preventable adverse events in elderly patients: population based review of medical records[END_REF] Defined as information systems where characteristics of individual patients are matched to a computerized knowledge base to generate patient-specific recommendations, [START_REF] Garg | Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review[END_REF] computerized decision support systems (CDSSs) have proven to be efficient in detecting medical errors and improving care quality in both hospital settings and primary care centers. [START_REF] Roshanov | Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials[END_REF][START_REF] Kawamoto | Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success[END_REF] However, it is not clear whether CDSSs have the same efficiency in NHs and little is known about their impact on nursing practices and NH resident clinical outcomes. [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF] In the perspective of enriching the NetSoins EHR system, currently implemented in nearly half of the French NHs nationwide, with the development of a CDSS module to support the prevention and management of malnutrition, pressure ulcers, osteoporotic fractures, and drug prescription errors, we conducted a scoping review to identify published CDSSs implemented in NHs and assess the factors that contributed to their effectiveness regarding NH resident clinical outcomes and care delivery. Another objective was to study NH healthcare professional satisfaction when using a CDSS and their level of adoption of CDSSs in routine practice. Beyond preliminary results previously reported, [START_REF] Abdellatif | Clinical Decision Support Systems in Nursing Homes: A Scoping Review[END_REF] the objective of this paper is to present the complete methods, results, and discussion of the scoping review.

METHODS

Search strategy

We searched PubMed/MEDLINE for papers written in English language and published between January 2000 and February 2020. Search terms were selected to cover the two dimensions of interest, i.e., decision support systems and nursing homes. The query used MeSH terms, e.g., "decision support systems, clinical", "reminder systems", "expert systems", "geriatric nursing", "homes for the aged", "nursing homes", "residential facilities", and key words, e.g., clinical decision support tool, computer-based decision support, longterm care homes, care home, etc. Two of the authors (AA and CL) independently screened the titles and abstracts of the retrieved articles to assess their relevance based on eligibility criteria. Backward citation tracking was also performed to identify additional relevant articles. Full text papers were reviewed for articles that both reviewers finally considered relevant.

Study eligibility criteria

The following inclusion criteria were used for the selection of relevant studies: (i) the study had to evaluate the implementation of a CDSS in NHs, (ii) the CDSS used should have to provide patient-specific recommendations to be considered by a healthcare practitioner (e.g., physicians, pharmacists, nurses), (iii) the CDSS impact defined in terms of resident clinical outcomes or NH care delivery had to be assessed. Studies were excluded when (i) the described CDSS was used in hospitals or primary health care settings but not in NHs, (ii) the study described the use of a non-computerized CDSS, (iii) there was no assessment of the CDSS implementation.

Study appraisal and synthesis methods

We assessed the quality of selected studies using the Mixed Method Appraisal Tool (MMAT), [START_REF] Hong | The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers[END_REF] version 2018, a qualitative scale that yields a 5-value score (0%, 25%, 50%, 75%, or 100%). The tool evaluates the aim of a study, its adequacy to the research question, the methodology used, the study design, participant recruitment, data collection, data analysis, presentation of findings, and the discussion and conclusion sections of the article. Studies were considered to be of "Low quality", "Medium quality", and "High quality" when the MMAT score was resp. 0 or 25%, 50% or 75%, and 100%.

Selected papers were first manually characterized using the following criteria: design of the study, type of the study, MMAT score, CDSS recipients (number of residents, number of facilities, patient characteristics), CDSS target users (nurses, pharmacists, physicians), CDSS features, and CDSS outcomes (including clinical outcomes and process outcomes). Inter-rater disagreements were solved by consensus.

Since we didn't retrieve enough studies reporting the same outcome in a similar way and because studies were not sufficiently homogeneous regarding the patients and the CDSSs studied, we didn't pool data in a random-effects meta-analysis. However, we narratively described the findings of included studies, organizing them according to the special issue addressed by the CDSS and presenting their results on: a) clinical outcomes, b) care delivery, and c) other criteria including users' satisfaction and adoption.

RESULTS

Article selection

The search query returned 1,809 references. Analysis of titles and abstracts discarded 1,747 references that were not relevant to the research question. A manual analysis of the citations of the remaining 62 articles was performed and identified an additional set of 19 references. We finally reviewed 81 full-text articles among which we excluded 57 papers that did not satisfy eligibility criteria, e.g., five studies [START_REF] Hussain | Recommendations service for chronic disease patient in multimodel sensors home environment[END_REF][START_REF] Jones | Developing Mobile Clinical Decision Support for Nursing Home Staff Assessment of Urinary Tract Infection using Goal-Directed Design[END_REF][START_REF] De Wit | Development of a computer system to support medication reviews in nursing homes[END_REF][START_REF] Esteves | A Proof of Concept of a Mobile Health Application to Support Professionals in a Portuguese Nursing Home[END_REF][START_REF] Gonzalvo | Supporting clinical rules engine in the adjustment of medication (SCREAM): protocol of a multicentre, prospective, randomised study[END_REF] were removed because information was missing to assess CDSSs. Finally, 24 articles were included for the final analysis. The corresponding PRISMA flow diagram is depicted in Figure 1.

General overview

Included studies (n=24) describe 20 different unique CDSSs implemented to support NH resident care quality. We have classified the papers according to the issue addressed by the CDSS as follows: malnutrition and pressure ulcer prevention and management (n=7), [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Sharkey | Exploratory study of nursing home factors associated with successful implementation of clinical decision support tools for pressure ulcer prevention[END_REF][START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF][START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF][START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF] drug prescription (n=8), [START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF][START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF] medication review (n=6), [START_REF] Sandral | Physician perceptions of consultant pharmacist services associated with an intervention for adverse drug events in the nursing facility[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] Gustafsson | Nurses' use of computerised decision support systems affects drug monitoring in nursing homes[END_REF][START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF][START_REF] Johansson-Pajala | Conditions for the successful implementation of computer-aided drug monitoring from registered nurses' perspective-a case site analysis[END_REF][START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF] and disease management (n=3). 40- 42 Studies were primarily conducted in the United States (n=12), [START_REF] Sharkey | Exploratory study of nursing home factors associated with successful implementation of clinical decision support tools for pressure ulcer prevention[END_REF][START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF][START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF][START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF][START_REF] Sandral | Physician perceptions of consultant pharmacist services associated with an intervention for adverse drug events in the nursing facility[END_REF][START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF][START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF][START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] in European countries (n=9), [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF][START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] Gustafsson | Nurses' use of computerised decision support systems affects drug monitoring in nursing homes[END_REF][START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF][START_REF] Johansson-Pajala | Conditions for the successful implementation of computer-aided drug monitoring from registered nurses' perspective-a case site analysis[END_REF][START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF] namely Sweden (n=4), [START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] Gustafsson | Nurses' use of computerised decision support systems affects drug monitoring in nursing homes[END_REF][START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF][START_REF] Johansson-Pajala | Conditions for the successful implementation of computer-aided drug monitoring from registered nurses' perspective-a case site analysis[END_REF] Norway (n=3), [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF] Belgium (n=1), [START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF] and the Netherlands (n=1). [START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF] Three studies were conducted in Canada. [START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF] Studies follow different designs, with six cluster randomized controlled trials (RCTs) [START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF][START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Sandral | Physician perceptions of consultant pharmacist services associated with an intervention for adverse drug events in the nursing facility[END_REF] , four nonrandomized controlled trials, [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF][START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF][START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF] 13 observational studies, [START_REF] Sharkey | Exploratory study of nursing home factors associated with successful implementation of clinical decision support tools for pressure ulcer prevention[END_REF][START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] Gustafsson | Nurses' use of computerised decision support systems affects drug monitoring in nursing homes[END_REF][START_REF] Johansson-Pajala | Conditions for the successful implementation of computer-aided drug monitoring from registered nurses' perspective-a case site analysis[END_REF][START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF][START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF][START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF][START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] and one ancillary study. [START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF] According to the MMAT score, a majority of the study are of high 20- 23,26,30,36,37,42 or of medium quality (i.e., MMAT score of 75% [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF][START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] Johansson-Pajala | Conditions for the successful implementation of computer-aided drug monitoring from registered nurses' perspective-a case site analysis[END_REF] or 50% [START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF][START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF][START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF] ).

Only two studies [START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF][START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF] are scored of low quality, both with a MMAT score of 25%. None was excluded from the analysis based on the MMAT score. Full details of quality and bias risk assessment are provided for each study in the supplementary material (Table s1, Table s2).

Description of the studied CDSSs

In all cases, CDSSs are computerized, knowledge-based systems providing decision support generated from clinical practice guidelines or experts' recommendations. Fourteen CDSSs are developed as software either embedded within EHRs (n=8), [START_REF] Sharkey | Exploratory study of nursing home factors associated with successful implementation of clinical decision support tools for pressure ulcer prevention[END_REF][START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF][START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Sandral | Physician perceptions of consultant pharmacist services associated with an intervention for adverse drug events in the nursing facility[END_REF][START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF][START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF][START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF] or connected to computerized provider order entry (CPOE) systems (n=6), [START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF][START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] leading to decision support automatically triggered from data input in EHR or CPOE systems. The remaining six CDSS [START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF] are standalone systems requiring users to re-enter patient data in a separate software.

CDSSs applied to malnutrition and pressure ulcer prevention and management aim at supporting nurses in the implementation of clinical practice guidelines, e.g., repositioning standards of care. Three out of seven CDSSs [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF] display alerts automatically triggered from risk assessment instruments, e.g., the Risk Assessment Pressure Scale for pressure ulcer risk screening, or the Mini Nutritional Assessment (MNA) scale to assess the nutritional status.

Risk assessment is continuously updated from new input data, which triggers alerts when the computed risk exceeds a certain threshold. More recently, based on connected devices, Yap et al. [START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF] developed a CDSS operating from data on the frequency and position of residents, wirelessly transferred from sensors to estimate nurse practices before and after visual monitors were activated.

CDSSs applied to drug prescription and medication review are usually implemented as CPOE systems identified as a means to improve medication safety. [START_REF] Mahoney | Effects of an integrated clinical information system on medication safety in a multi-hospital setting[END_REF] They usually display warning messages to alert physicians on non-compliant drug prescription. Subramanian et al. [START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF] proposed a system for NH residents with renal insufficiency able to display four categories of alerts to show the recommended doses, the recommended frequencies, the drugs to be avoided, and when additional information was needed to compute creatinine clearance.

Colón-Emeric et al. [START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] developed an alert-based CPOE system based on clinical practice guidelines to manage geriatric problems in NHs, e.g., falls, fever, pneumonia, urinary tract infection, and osteoporosis. Alexander [START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF] developed a CDSS to alert clinicians about changes in resident condition either based on episodic events (e.g., a dehydration alert is sent to a provider if an episode of bowel incontinence is reported within a 24-hour period) or successive clinical assessments (e.g., a decline in condition alert is sent when the resident ability to make decisions declines over two successive systematic evaluations).

CDSSs alerts are usually displayed at the moment of the prescription, [START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF] but they could also be sent as emails, or used in phone calls between pharmacists and physicians. For instance, de Wit et al. [START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF] developed a standalone pharmacy CDSS for medication review that automatically triggers alerts to the pharmacist who then had to contact the prescriber physician to indicate how to revise the mis-prescription. Johansson-Pajala et al, [START_REF] Gustafsson | Nurses' use of computerised decision support systems affects drug monitoring in nursing homes[END_REF][START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF][START_REF] Johansson-Pajala | Conditions for the successful implementation of computer-aided drug monitoring from registered nurses' perspective-a case site analysis[END_REF] proposed a CDSS as a web application connected to the NH EHR system. When using the CDSS, registered nurses could assess patient symptoms and initiate the medication review to be sent to the physician who then could make the final decision. Zhu et al. [START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF] developed a CDSS applied to heart failure management which includes various reporting tools to help data tracking and analysis to be used by physicians (tabular view of medications, interactive trending graphs for weight tracking over time, and charts to support visual symptom analysis).

Effects of CDSSs on malnutrition and pressure ulcer prevention and management

Seven articles [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Sharkey | Exploratory study of nursing home factors associated with successful implementation of clinical decision support tools for pressure ulcer prevention[END_REF][START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF][START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF][START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF] assessed the implementation of five different CDSSs to support pressure ulcer and malnutrition prevention and management (see Table 1). There is only one cluster RCT [START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF] including 464 residents and 118 NH staff. The aim of the intervention is to assess the impact of the CDSS on nursing practice compliance with pressure ulcer prevention in NHs. A significantly increased number of patients (from 13.2 % to 60.0 % within 120 days, p=0.003) actually received the recommended pressure ulcer prevention when there were seating in a chair. However, no effect was found on the allocation of preventive measures when residents at risk were lying in bed. [START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF] This study also reports a significant improvement in the attitude of healthcare professionals towards pressure ulcer prevention (from 74.3% to 83.5%, p=0.001). [START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF] However, no clinical outcome related to the decrease of pressure ulcer prevalence was found and no significant improvement was observed in healthcare professional knowledge, [START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF] as assessed by a validated pressure ulcer Knowledge Assessment Tool. [START_REF] Beeckman | Pressure ulcer prevention: Development and psychometric validation of a knowledge assessment instrument[END_REF] Three studies are non-randomized controlled trials, [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF][START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF] cumulating 7,623 residents and 28 nurses. Clinical outcomes are assessed in two studies. In these studies, using the CDSS significantly lowered the prevalence of malnutrition among NH residents (from 28.8% to 19.8%, p=0.05) [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF] and significantly decreased pressure ulcer incidence (59% reduction in a month, p=0.035). [START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF] Cost reduction (approximately US$208 saved per month and per resident) was also observed. [START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF] The third study concludes on the better documentation of nurse assessment of pressure ulcers (from 25 % to 88 %, p<0.05) and nutritional issues (from 20% to 100%, p=0.007) [START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF] in patient records when using the CDSS.

The three remaining studies are observational studies, [START_REF] Sharkey | Exploratory study of nursing home factors associated with successful implementation of clinical decision support tools for pressure ulcer prevention[END_REF][START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF] involving 2,146 residents and 63 nurses in total. The use of the CDSS was associated with an improvement of care delivery, e.g., a better compliance of nursing care with repositioning clinical practice guidelines (from 7.0 % to 100 % during a 3-day baseline period and from 50.6 % to 100 % during the 18-day intervention p=0.0003). [START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF] However, no significant change in nursing culture (assessed by the Nursing Culture assessment tool [START_REF] Yap | Nursing culture assessment tool (NCAT): Empirical validation for use in long-term care[END_REF] ) was reported. [START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF]

Effects of CDSSs on drug prescription and medication review

Drug prescription

The implementation of CDSSs supporting daily drug prescription is described in eight studies, [START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF][START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF] that report the results of the assessment of eight different CDSSs (see Table 2).

Six studies enroll physicians as primary users of the CDSS. [START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF][START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF] Pharmacists are the principal users in the two other studies. [START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF] Four studies are cluster RCTs, 26,29-31 cumulating 3,209 resident with physicians and pharmacists as users. No clinical outcomes are reported in these studies. Gurwitz et al., [START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF] observed no significant reduction of adverse drug events (ADEs) or preventable ADEs. The other cluster RCTs reported an improvement of drug prescribing in response to alerts generated by the CDSS. Judge et al., [START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF] observed that alerts were most likely to generate appropriate action, e.g., ordering a recommended laboratory test or canceling an inappropriate ordered drug (25 % with the CDSS vs 7% in the control unit, relative risk 1.11, 95% confidence interval [1.00, 1.22]). Donovan et al., [START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF] reported that psychotropic medication orders, modified in response to alerts, were significantly improved. Final drug orders were appropriate significantly more often in the intervention units as reported by Field et al. [START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF] (increase of recommended administration frequencies, but no improvement of the appropriate doses, and decrease of drugs that were recommended to avoid in NHs).

However, Donovan et al., [START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF] found no overall improvement of the quality of psychotropic medication prescriptions with the use of a CDSS.

Three studies are observational studies, [START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF] including 1,598 residents and 17 users.

Papaioannou et al., [START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF] reported no significant increase of the percentage of INR in the therapeutic range for resident on warfarin. However, CDSSs supporting daily drug prescription showed an impact on practices beyond clinical outcomes. Handler et al., [START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF] implemented a clinical event monitoring system using signals to detect potential ADEs that allowed to improve the detection of ADEs at levels that are substantially higher than the rates reported in the literature (53% for antidote signals and 96% for laboratory/medication signals). Papaioannou et al. [START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF] showed that using a CDSS significantly decreased the average number of INR tests performed per month (from 4.2 to 3.1 per resident, p < 0.0001), while at the same time either maintaining or improving the quality of Warfarin management.

Similarly, Kennedy et al. [START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF] reported that physicians responded to 70% of the alerts with an appropriate dose change or medication discontinuation when using the CDSS.

The last study is an ancillary study performed in a cluster-randomized trial [START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF] studying 10 users that concluded on a modest reduction of costs (US$4.71 per resident and per year) partially offset by an increase in laboratory-related costs. [START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF]

Medication review

Six studies, [START_REF] Sandral | Physician perceptions of consultant pharmacist services associated with an intervention for adverse drug events in the nursing facility[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] Gustafsson | Nurses' use of computerised decision support systems affects drug monitoring in nursing homes[END_REF][START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF][START_REF] Johansson-Pajala | Conditions for the successful implementation of computer-aided drug monitoring from registered nurses' perspective-a case site analysis[END_REF][START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF] evaluate the role of CDSSs to improve medication review (see Table 3). None of them evaluate CDSS clinical outcomes. One cluster RCT, [START_REF] Sandral | Physician perceptions of consultant pharmacist services associated with an intervention for adverse drug events in the nursing facility[END_REF] including 141 residents and 60 pharmacists and physicians showed an improved physicians' assessment of the importance and performance of consultant pharmacists services. In this study, importance rating increased for all 24 survey questions, and five of the changes were statistically significant (p<0.05).

One non-randomized trial, [START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF] including 54 residents and 14 nurses describes a CDSS to be used by nurses to initiate and prepare medication reviews. [START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF] CDSS detected significantly more drug-related issues (p=0.008) than nurses, but nurses detected additional relevant problems that were outside the scope of the CDSS, e.g., the lack of adherence. In addition, CDSS allowed to improve the quality of prescriptions with a decreased number of renally excreted drug orders in residents with reduced renal function.

The four remaining studies are observational studies, [START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] Gustafsson | Nurses' use of computerised decision support systems affects drug monitoring in nursing homes[END_REF][START_REF] Johansson-Pajala | Conditions for the successful implementation of computer-aided drug monitoring from registered nurses' perspective-a case site analysis[END_REF][START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF] cumulating 1,425 residents and 36 users (12 physicians and pharmacist and 24 nurses). Using a CDSS evidenced some positive outcomes on care delivery processes. Ulfvarson et al., [START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF] reported that the CDSS improved the quality of drug use with the reduction or the elimination of dangerous or improper prescriptions (reduction of anticholinergic drugs by 40%, of long-acting benzodiazepines by 17%, correction of drug duplication by 30%). When using the system to support medication review, the number of drugs used decreased from 10.4 to 9.5 drugs per patient, resulting in a more cost-effective drug therapy (decreased drug costs of 149 euros per patient over a 10-month period), while preserving the same level of care quality. De Wit et al., [START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF] studied the relevance of alerts generated by the CDSS and reported that only 3.6% of alerts were considered as clinically relevant, with non-relevant alerts related to care already delivered.

Effects of CDSSs on disease management

Disease management was applied to heart failure, falls, and chronic diseases, e.g., Alzheimer disease. Results are displayed in Table 4. Three studies assess the use of a CDSS for disease management in NHs. [START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF][START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF][START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] All three studies are observational studies and describe three different systems cumulating 783 residents and 47 NH staff.

No study involving the use of a CDSS for disease management evaluate the impact of the system on patient clinical outcomes. However, the OneTouch system [START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF] that automatically triggers clinical alerts to support identifying when a resident might be experiencing some change in condition, e.g., constipation, dehydration, skin integrity change, weight loss, weight gain, was beneficial in decreasing pressure ulcer prevalence (from 20% to 9%) and pain (from 14% to 6% ) as a side effect. Process outcomes were assessed in the two other studies. [START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF][START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] Colón-Emeric et al. [START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] showed that using a guideline-based CDSS applied to the management of five common problems in NHs (falls, fever, pneumonia, urinary tract infection, and osteoporosis) could improve the collection of quality measures for blood pressure (from 17.5% to 30.0%, p=0.29), neuroleptic prescription (from 53.8% to 75%, p=0.27), sedative-hypnotics prescription (from 16.7% to 50.0%, p=0.50), calcium prescription (from 22.5% to 32.5%, p=0.45), vitamin D prescription (from 20.0% to 35.0%, p=0.21), and external hip protectors (from 25.0% to 47.5%, p=0.06). Zhu et al. [START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF] developed a CDSS following the Heart Failure national guidelines for the weekly monitoring, evaluation, and management of care for patients suffering from heart failure. This included documentation of left ventricular function, weight changes and specific symptoms tracking, medication titration, discharge instructions, 7-day follow up appointment post NH discharge, and patient education. Using the CDSS showed to have beneficial process outcomes, e.g., the authors observed that data capture was improved as compared to paper-based practices, and order input was enhanced (auto completion of medication name, easy retrieval of medication history, easy medication modification). [START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF] Besides, Zhu et al., [START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF] reported that real time data capture when using the CDSS was beneficial to avoid errors and allowed to save time by the automatic computation of clinical scores.

CDSS users' acceptance

Users' acceptance of CDSSs was rarely investigated in the retrieved studies, [START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF][START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF][START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF][START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] and when investigated, results were controversial.

A good acceptance of the CDSS was reported in six studies [START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF][START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF] where users considered that the CDSS was a supportive work environment tool, easy to use, and useful. [START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF] In a survey, Papaioannou et al. [START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF] reported CDSS users thought the CDSS decreased workload (75% of participants), improved healthcare professional confidence in patient management and drug decisions (80% of participants), enhanced teamwork (67% of participants) and communication (92% of participants). Ulfvarson et al., [START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF] reported the same conclusion that users considered the CDSS was successful to improve teamwork between healthcare professionals. However, five studies [START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF][START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF][START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] reported a poor acceptance of CDSS users. Fossum et al., [START_REF] Fossum | An evaluation of the usability of a computerized decision support system for nursing homes[END_REF] observed some resistance to using computers and a limited integration of the CDSS within the facility's EHR. Alexander et al., [START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF] reported multiple unnecessary alerts due to the nondocumentation of actually given care, criticism expressed towards the lack of standardization in terminologies, [START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF] a poor use of the CDSS by NH staff except for the management of falls (in 73% of the cases) although the system was recognized as a means to improve the training of new staff. [START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] Similarly, de Wit et al., [START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF] highlighted the alert fatigue phenomenon with only 3.6% of alerts considered as actually clinically relevant.

Qualitative methods have been used to assess nurses' position towards the use of CDSSs.

Nurses' expectations stated that the CDSS should help saving time, provide some clinical work standardization, support knowledge acquisition, and contribute to a better division of responsibilities between nurses and physicians. [START_REF] Gustafsson | Nurses' use of computerised decision support systems affects drug monitoring in nursing homes[END_REF] Nurses' recommendations for a successful CDSS implementation were that the CDSS should allow saving time, curbing administrative hassle, improving collaboration at all levels, and identifying responsibilities and roles. There should also be a strong governance involvement. [START_REF] Johansson-Pajala | Conditions for the successful implementation of computer-aided drug monitoring from registered nurses' perspective-a case site analysis[END_REF] The use of CDSSs in routine practice was not described in the selected papers, thus CDSS adoption beyond the intervention study period is not discussed in this paper.

DISCUSSION

Despite an exhaustive search, the scoping review identified only 24 studies reporting on the use of a CDSS in NHs, among which only six are controlled trials. This low number contrasts with those of literature reviews on CDSSs that did not restrict to NHs. For instance, with respect to CDSS impact, Bright et al., [START_REF] Bright | Effect of clinical decision-support systems: a systematic review[END_REF] in 2012 included 148 RCTs, Roshanov et al., [START_REF] Roshanov | Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials[END_REF] in 2013 included 162 RCTs, and more recently in 2020, Kwan et al., [START_REF] Kwan | Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials[END_REF] analyzed 108 randomized or quasi-randomized trials. Such an imbalance confirms that NH CDSSs have been poorly investigated yet, meaning that probably CDSSs are less implemented in NHs than in other healthcare settings, and that evidence regarding current CDSS impact in NHs would be less robust. In our study, only four RCTs reported a positive impact of the CDSS on care delivery concerning the prevention of pressure ulcers and adverse drug events.

Besides, included papers are rather old, published before 2015, except those used to support medication review. Most of the selected articles (58%) are applied to drug prescription and reviewing. This can be explained by the fact that older adults are subject to multimorbidities and polymedication, and consequently very likely to experience adverse drug events. [START_REF] Naples | Recent Literature on Medication Errors and Adverse Drug Events in Older Adults[END_REF] The other studies are mainly applied to the management of pressure ulcers and malnutrition, with a few of them applied to disease management. These findings about the relatively scare literature and the focus on medication are broadly consistent with those of other published reviews on the subject. Thus, a recent systematic review and meta-analysis of RCTs assessing interventions that increase the appropriateness of medications used in NHs only retrieved two interventions including a CDSS. [START_REF] Almutairi | Optimisation of medications used in residential aged care facilities: a systematic review and meta-analysis of randomised controlled trials[END_REF] In 2015, Marasinghe [START_REF] Marasinghe | Computerised clinical decision support systems to improve medication safety in long-term care homes: a systematic review[END_REF] published a systematic review on CDSSs in long-term care homes, but the focus, restricted to medication safety, was not as broad as in this scoping review. Only seven papers met the inclusion criteria, leading the author to the conclusion of limited literature on the subject. It is of note that among the seven studies selected by Marasinghe, six reported on CDSSs for daily drug prescription [START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF] that we also retrieved and included within our review. The seventh paper was not related to NHs but to adults aged 65 and older.

The impact of CDSSs in terms of clinical outcomes for NH residents was assessed in only one quarter of all studies. [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF][START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF][START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF] Three of them reported positive effects: a decrease in malnutrition prevalence, [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF] a decrease in pressure ulcer incidence, [START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF] and a decrease of pain prevalence. [START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF] Two studies reported no impact of the CDSS on pressure ulcer prevalence, [START_REF] Fossum | Effects of a computerized decision support system on pressure ulcers and malnutrition in nursing homes for the elderly[END_REF][START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF] and one study showed no impact on the reduction of adverse drug events. [START_REF] Gurwitz | Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting[END_REF] From this limited number of studies, the impact of CDSSs on residents' clinical outcomes appears to be poorly studied and, when studied, it remains limited. [START_REF] Garg | Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review[END_REF] Otherwise, improvements of care delivery were observed in almost all included studies. [START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF][START_REF] Fossum | Effects of a computerized decision support system on care planning for pressure ulcers and malnutrition in nursing homes: An intervention study[END_REF][START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF][START_REF] Field | Computerized clinical decision support during medication ordering for long-term care residents with renal insufficiency[END_REF][START_REF] Papaioannou | A team-based approach to warfarin management in long term care: A feasibility study of the MEDeINR electronic decision support system[END_REF][START_REF] Kennedy | Piloting a renal drug alert system for prescribing to residents in long-term care[END_REF][START_REF] Donovan | Effect of clinical decision support on psychotropic medication prescribing in the long-term care setting[END_REF][START_REF] Judge | Prescribers' responses to alerts during medication ordering in the long term care setting[END_REF][START_REF] Handler | Assessing the Performance Characteristics of Signals Used by a Clinical Event Monitor to Detect Adverse Drug Reactions in the Nursing Home[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF][START_REF] Martin | Registered nurses' use of computerised decision support in medication reviews[END_REF][START_REF] Zhu | DCDS: A Real-time Data Capture and Personalized Decision Support System for Heart Failure Patients in Skilled Nursing Facilities[END_REF][START_REF] Colón-Emeric | Development and pilot testing of computerized order entry algorithms for geriatric problems in nursing homes[END_REF] The benefits reported are similar to those already observed in hospital settings, 51 e.g., improvement of drug order quality, better compliance with guidelines (for pressure ulcer prevention), and enhanced documentation of care records. In addition, studies showed that CDSSs were useful and cost effective; [START_REF] Olsho | Evaluation of ahrq's on-time pressure ulcer prevention program[END_REF][START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF] they allowed for time saving, they improved communication and healthcare professional commitment, 22 they increased teamwork. [START_REF] Subramanian | Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study[END_REF][START_REF] Ulfvarson | Medication reviews with computerised expert support: Evaluation of a method to improve the quality of drug utilisation in the elderly[END_REF] However, no improvement of healthcare professional knowledge [START_REF] Beeckman | A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial[END_REF] or nursing culture [START_REF] Yap | Pressure injury prevention: outcomes and challenges to use of resident monitoring technology in a nursing home[END_REF] was reported.

It should be noticed that almost all systems were evaluated by their own developers and it has been reported that such evaluations are more likely to show benefit than evaluations conducted by third parties. [START_REF] Roshanov | Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials[END_REF] Moreover, most CDSSs were embedded within an EHR or a CPOE system. This allows for system interoperability, unique data entry, multiple data reuse, and enables workflow integration, characteristics which are known to increase users' acceptance and CDSS use as compared to standalone CDSSs. [START_REF] Roshanov | Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials[END_REF] CDSSs are generally poorly described in the studies retrieved by the scoping review and it is difficult to know how these systems were developed (guideline modeling, knowledge base implementation, data processing, user's interfaces, etc.) and how they were operating. Kawamato et al., [START_REF] Kawamoto | Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success[END_REF] attempted to address the question of which CDSS features contribute to making them more effective in a meta-analysis of 70 studies. In particular, they stressed the importance of a decision-support recommendation accompanying warnings rather than a simple assessment or alert. Lobach et al., [START_REF] Lobach | Enabling health care decisionmaking through clinical decision support and knowledge management[END_REF] pointed out that when CDSSs did not require the entry of new information, they were more likely to be adopted. However, CDSS limits such as alert fatigue, distraction, or user hostility have been reported. [START_REF] Lobach | The road to effective clinical decision support: are we there yet?[END_REF] The main pitfalls of issuing too many clinically-non-relevant alerts are a systematic overriding, CDSS disconnection, or clinician burnout. [START_REF] Co | The tradeoffs between safety and alert fatigue: Data from a national evaluation of hospital medication-related clinical decision support[END_REF] In our work, NH users' acceptance, when assessed, was judged satisfactory though alert fatigue was explicitly reported in two studies. [START_REF] De Wit | Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients[END_REF][START_REF] Alexander | A Descriptive Analysis of a Nursing Home Clinical Information System with Decision Support[END_REF] Finally, the small number of studies and the diversity of CDSSs and of study designs made difficult the identification of factors contributing to the adoption of CDSSs specifically designed for NHs.

As with any literature review, this scoping review presents some limitations related to publication bias: some studies reporting on the implementation of a CDSS in NHs may have not been published, especially if the results were negative. Another potential limitation is that the studies we found are heterogeneous, and we have gathered them by type of clinical application to identify general trends in their effects. The methodological design and quality of the studies were also heterogeneous. We chose not to exclude any studies because of poor methodological scores, as studies evaluating CDSS are much more difficult to conduct than traditional double blind drug evaluation.

Conclusions and implications

We performed a scoping review of the literature to identify studies of CDSSs implemented in NHs and assess CDSS factors impacting NH resident clinical outcomes and care delivery.

Whether embedded within an EHR or not, used by physicians, nurses, or pharmacists, generating alerts or not, this review suggests CDSSs may improve healthcare professional daily practice and resident clinical outcomes in several domains especially pressure ulcer prevention and drug prescription improvement. However, we could not draw robust conclusions from the evidence reported in the studies retrieved due to the variability in CDSS design, intervention protocols, outcomes, and the limited number of included studies. Therefore, more good quality studies are needed to assess further initiatives such as the deployment of CDSSs able to detect critical conditions, alert practitioners during care processing, provide recommendations to make decisions easier, or simply remind actions not-to-be missed, all functionalities being rather promising in the NH context. Besides, CDSS effects on residents' clinical outcomes should be more extensively investigated in future studies.

--

  Ease of use, usefulness, and supportive work environment -Lack of training, resistance to using digital tools, limited integration of the CDSS within the EHR, poorly designed graphical High level of CDSS use for pressure ulcer prevention in 36% of NHs -Significant involvement of NH directors and high participation of nurse managers * The three studies by Fossum et al. used the same CDSS but were focused on different assessment facets.

Non

  -Alert fatigue due to non-clinically relevant alerts -Unnecessary alerts related to care already delivered but not documented in patient records Kane-Gill et al.expectations from CDSSs include time saving, clinical work standardization, knowledge acquisition, and a better division of responsibilities between nurses and physicians *Johansson-Pajala et al.recommendations for a positive CDSS implementation include time saving, curbing administrative hassle, improved collaboration at all levels, identifying responsibilities and roles, and governance involvement

  Figure 1: Prisma flow diagram

Table 1 :

 1 CDSSs for malnutrition and pressure ulcer prevention and management

	538
	539
	Reference	Study design	MMAT rating	Residents (R), End-Users (U) Facilities (F), and	Clinical Outcomes	Process Outcomes	Other outcomes
	Olsho et al., 2014,	Non-randomized	100%	-R = 3,463 + 2,698	-Significant reduction of	-Not studied	-Cost saving ($20,800 per month per NH, 100 residents)
	USA 21	controlled trial		-F = 12 + 13	pressure ulcer incidence
				-U = NA (Nurses)	(59% reduction in monthly
					incidence, p = 0.035)
					-Significant decrease of
					malnutrition prevalence
	*Fossum et al., 2011, Norway 12	Non-randomized controlled trial	75%	-R = 491 -U = 28 (Nurses) -F = 15	(from 28.8% to 19.8%, -No significant decrease of p=0.05)	-Not studied	-Not studied
					pressure ulcer prevalence
						-Improved pressure ulcer
	Beeckman et al., 2013, Belgium 25	Cluster-randomized controlled trial	75%	-R = 464 -F = 4 -U = 118 (NH staff)	-No significant decrease of pressure ulcer prevalence	prevention when residents are seated in a chair (p=0.003) -No significant increase of	-Significant improvement of the attitude of healthcare professionals towards pressure ulcer prevention (p=0.001) -No significant improvement of healthcare professional
						pressure ulcer prevention when	knowledge
						residents are lying in bed
						-Improved documentation of
	*Fossum et al., 2013, Norway 24	Non-randomized controlled trial	75%	-R = 971 -U = NA (Nurses) -F = 15	-Not studied	pressure ulcer (from 25% to nursing assessment (from 20% to 88%) and nutritional status	-Not studied
						100%)
	Yap et al., 2019,	Observational study	100%	-R = 44	-Not studied	-Significant improvement of	-Improved communication and professional commitment
	USA 22	(pre/post design)		-F = 1		compliance with repositioning	(p=0.035)
				-U = 38 (Nurses)		clinical practice guidelines	-Satisfaction with the CDSS implementation (focus groups)
						(p=0.0003)	-No improvement of nursing culture
	*Fossum et al.,
	2011, Norway 23

Table 2 :

 2 CDSSs for daily drug prescription 540 The three studies by Johansson-Pajala et al. used the same CDSS but were focused on different assessment facets.

	Reference	Study design	MMAT	Residents (R), Facilities (F),	Clinical Outcomes	Process Outcomes	Other outcomes
			rating	and End-Users (U)
	Papaioannou et	Observational study	75%	-R = 128	-No significant increase	-Significant decrease of the average number of INR	-Decreased workload,
	al., 2010,	(pre/post design and		-F = 6	of the percentage of INR	tests per month (p < 0.0001)	improved healthcare
	Canada 27	focus groups)		-U = 12 (NH staff, pharmacist)	in the therapeutic range		professional confidence in
							patient management and
							drug decisions, improved
							teamwork, improved
							communication
	Gurwitz et al.,	Cluster-randomized	100%	-R = 1,118	-No significant decrease	-Not studied	-Not studied
	2008, USA 30	controlled trial		-F = 2	of ADEs and preventable
				-U = NA	ADEs
				(Pharmacists, physicians)
	Judge et al.,	Cluster-randomized	75%	-R = 445	-Not studied	-Alerts were more likely to engender an appropriate	-Not studied
	2006, USA 31	controlled trial		-F = 1		action such as ordering a recommended laboratory
				-U = NA (Physicians)		test or canceling an ordered drug (RR=1.11, 95% CI =
						[1.00 ; 1.22])
	Handler et al.,	Observational study	75%	-R = 274	-Not studied	-ADEs detected in NHs with a high degree of accuracy	-Not studied
	2008, USA 32	(cohort)		-F = 1		using a clinical event monitor.
				-U = NA (Physicians)		-Overall PPV for all signals = 81%
						-Of the true positive findings, one-third of
						ADEs considered as preventable.
						-Of the preventable ADEs, 88% occurred at the
						monitoring and 69% at the prescribing stages.
	Field et al., 2009,	Cluster-randomized	100%	-R = 833	-Not studied	-Higher proportions of final drug orders
	Canada 26	controlled trial		-F = 1 (22 units)		were appropriate in the intervention units
				-U = NA (Physicians)		(RR=2.4, 95% CI = [1.4, 4.4] for maximum frequency;
						RR=2.6, 95% CI = [1.4, 5.0] for drugs that should be
						avoided; and RR=1.8, 95% CI = [1.1, 3.4] for alerts to
						acquire missing information).
						-Final drug orders were appropriate
						significantly more often in the intervention
						units (RR=1.2, 95% CI = [1.0, 1.4])
						-No improvement of drug orders with appropriate
						doses (e.g., no dose adaptation for residents with
						renal insufficiency)
	Donovan et al.,	Cluster-randomized	50%	-R = 813	-Not studied	-Significant improvement of psychotropic medication	-Not studied
	2010, USA 29	trial		-F = NA		orders in response to alerts (8% in intervention unit
				-U = NA (Physicians)		vs. 2% in control unit, RR = 3.69, 95% CI =
						[1.08,12.57])
						-No overall improvement of prescribing quality
	Kennedy et al.,	Observational study	50%	-R = 1,196	-Not studied	-Physicians responded to 70% of the alerts with a	-Alerts considered as
	2011, Canada 28	(cohort)		-F = 7		dose change or medication discontinuation.	helpful

Table 3 :

 3 CDSSs for medication review 544

	Reference	Study design	MMAT	Residents(R), Facilities	Clinical Outcomes Process outcomes	Other outcomes
			rating	(F), and End-Users (U)
	Ulfvarson et al.,	Observational study	75%	-R = 275	-Not studied	-Improved quality of drug	-Increased teamwork and awareness of the
	2010, Sweden 35	(pre/post design)		-F = 7		use: reduction of	importance of detecting ADEs
				-U = 12 (Physicians)		anticholinergic drugs (40%),	-Cost saving (143 € per patient/ 10 months)
						long acting benzodiazepines
						(17%), drug duplication
						(30%), drugs that should be
						avoided in patients with
						impaired kidney function
						(17%), number of drugs per
						patient (from 10.4 to 9.5)
	*Johansson-Pajala et
	al., 2018, Sweden 37

Table 4 :

 4 CDSSs for disease management

	Reference	Study design	MMAT	Residents (R),	Clinical outcomes	Process outcomes	Other outcomes
			rating	facilities (F) and
				End-users (U)
	Alexander, 2008, USA 40	Observational study (pre/post design)	50%	-R = 518 -F = 3	-Decreased prevalence of pressure ulcers (from 20%	-Not studied	-Moderate adoption of the CDSS -Unnecessary alerts related to care already delivered
				-U = NA (NH Staff)	to 9%) and pain (from 14%		but not documented in patient records
					to 6%)		-Staff frustration because of the lack of
							standardization in terminologies
	Colón-Emeric et al.,
	2009, USA 42

Acknowledgments

This research was supported by the French National Association for Research in Technology (ANRT) CIFRE-Industrial Agreements for Research Training under the grant n° 2018/0307 and by Teranga Software, a French vendor of NH information systems.

Conflicts of interest: none for all the authors.