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impact uni vent ventilator service manualThus, QI programs will not result in optimal changes in staff behavior unless the context for social learning is present. Accordingly, we developed CONNECT, an intervention to foster systematic use of management practices, which we propose will enhance effectiveness of a nursing home Falls QI program by strengthening the staff-to-staff interactions necessary for clinical problem-solving about complex problems such as falls. Subjects (staff and residents) are clustered within nursing homes because the intervention addresses social processes and thus must be delivered within the social context, rather than to individuals. Nursing homes randomized to FALLS alone receive three months of FALLs QI and are offered CONNECT after data collection is completed. Complexity science measures, which reflect staff perceptions of communication, safety climate, and care quality, will be collected from staff at baseline, three months after, and six months after baseline to evaluate immediate and sustained impacts. FALLS measures including quality indicators (process measures) and fall rates will be collected for the six months prior to baseline and the six months after the end of the intervention. Analysis will use a three-level mixed model. By focusing on improving local interactions, CONNECT is expected to maximize staff's ability to implement content learned in a falls QI program and integrate it into knowledge and action. Our previous pilot work shows that CONNECT is feasible, acceptable and appropriate. The FALLS intervention is expected to provide nursing home staff members with the content needed to know what fall reduction assessments and interventions to use for residents at risk for falling. Increased use of these fall reduction assessments and interventions are expected, in turn, to reduce the fall rates and probability of recurrent falls among nursing home residents.http://www.tcco.com.tw/upload/editor/060658139044.xml
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CONNECT for quality: Protocol of a cluster randomized controlled trial to improve fall prevention in nursing homes.pdf Content available from CC BY 2.0: 1748-5908-7-11.pdf CONNECT for quality: Protocol of a cluster randomized controlled trial to improve fall prevention in nursing homes.pdf Available via license: CC BY 2.0 Content may be subject to copyright. Acco rdingly, we developed CO NNECT, an inter vention to fo ster system atic use of manag ement pract ices, which we p ropose will enhance effect iveness of a nur sing home Falls QI pr ogram by strengt hening the sta ff-to-st aff interact ions necessar y for clinical pro blem-sol ving about co mplex prob lems such as fa lls. Subjects (staff and residents) are clustered within nursing homes because the intervention addres ses social processes and thus must be delivered within the social context, rather than to individuals. Complexity science measures, which reflect staff perceptions of comm unication, safety climate, and care quality, will be collected from staff at baseline, three months after, and six months after baseline to evaluate immediate and sustained impacts. Discussion: By focusing on improving local interactions, CONNECT is expected to max imize staff ’ s ability to implement content learned in a falls QI program and integrate it into knowledge and action. Our previous pilot work shows that CONNECT is feasible, acceptable and appropriate.Reducing multiple risk factors may be difficult because it requires many staff members to have strong connections that permit effec- tive information flow and problem-solving from varied p e r s p e c t i v e s.T h u s,a ni n t e r v e n t i o ni sn e e d e dt oh e l p nursing home staff establis h relationship networks and communication channels to support the new practices introduced by QI programs. Complexity science provides useful insights for addressing barriers to effective staff interdependence. Staff at all levels used these MPs, but only erratically.http://intop.in.ua/userfiles/casio-1174-cmd-40-manual.xml Because of interdependence and self-organization, the intervention must be delivered within the social context in which individuals work, rather than to individuals a lone. Thus, a cluster rando- mization is needed. CONNECT is a multi-component intervention that helps staff: learn new strategies to improve day-to-day interactions; establish relationship networks for creative problem solvi ng; and sustain newly acquired interacti on behaviors through mentorsh ip. We propose that CONNECT, when combined with a content focused fal ls QI program (FALLS), will result in better resident outcomes when compared to FALLS alone. To complete these aims we will use a cluster rando- mized, controlled trial design with the facility-level change in fall and complexity science measures as the primary outcomes. Because the CONNECT and FALLS interventions both encourage facility-wide change in staff behavior, randomization will occur at the facility level and subjects (nursing home staff an d residents) are clustered within homes. Significance Improving resident outcomes in NHs remains a national priority. Complexity science suggests that a major bar rier to the effectivenes s of QI programs is their content focus; they do not impact the processes Anderson et al. Implementation Science 2012, 7:11 Page 2 of 14 The CONNECT for Quality study is significant because it will test a novel int ervention that attempts to create the foundation nee ded for nursing home staff to impl ement content learned in QI programs such as FALLS. Thus, CONNECT has the potential to have a broad and far- reaching impact on QI effort s nationally and influence care for multiple geriatric syndromes. Of further significance, this study uses exist ing staff to improve resident care, without requiring additional resources. CONNECT, if successful, thus has the potential to be generalizable to real-world nur- sing home settings by enhancing existing staff capacity to learn and improve.http://superbia.lgbt/flotaganis/1649375102 Further, this study is significant because it puts the tools of change into the hands of direct- care staff. CONNECT will es tablish networks for new informat ion about fall risk factor reduction to spread throughout the nursing home. CONNECT will create opportunities for more rapid information exchange and problem solving among multiple disciplines and will increase the likeli- hood that the NA will carry ou t appropriate fall preven- tion care. These new networks of connections allow local cha nges in behavior to result in syst em-wide change. We propose that sys tematic use of these local interac - tion strategies to create relation ship networks and chan- nels of communication for learning together, exchanging information, and solving problems, is a prerequisite to the ability to effectively implement a fall reduction pro- gram. Based on complexi ty science theory, if we achieve expected changes in staff interactions, we will observe changes in measures of communicat ion, participation in decision-making, relational coordination, psychological safety, and safety culture (Figure 1). These measures in turn are expected to be related to more effective fall risk factor reduction measures. CONNECT is expected to work in combination with QI programs because CONNECT creates processes for group learning and implementation of evidence-based content introduced by the QI program. Modifiable fall risk factors, suggested by clinical practice guidelines and Figure 1 Proposed Relationship between the FALLS and CONNECT Interventions. The CONNECT intervention, on the other hand, is expected to directly reduce the fall rates as well as increase the staff ’ s use of the fall reduction assessment and interventions, thus having a greater impact of fall reduction than the FALLS intervention alone. Anderson et al. Implementation Science 2012, 7:11 Page 3 of 14 Nur- sing homes randomized to FALLS alone will be offered CONNECT following data collection.http://charlescarteronline.com/images/caddx-nx-1324e-manual.pdf Complexity science measures, to be completed by nursing home staff, will be collected at baseline, and three and six months after baseline to e valuate the immediate and sustained impact on system parameters. FALLS mea- sures, collect ed from medical record review, will be co l- lected longitudinally for the six months prior to baselin e and the six months after the end of the intervention to allow adequate fall events to accrue. A five-year timeline is planned to compl ete the study. This study was review and approved by the Duke University Medical Center Institutional Review Board. Sample and setting Nursing home recruitment and randomization A sample of nursing homes will be draw n from 69 facil- ities in Nort h Carolina that particip ate in Medicare and Medicaid and are within a 100-mile radius of Duke Uni- versity. The North Carolina Qual- ity Improvement Organiza tion, the Carolinas Center for Medical Excellence (CCME), will recruit for our study. Eligible nursing homes will be contacted in random order by CCME until 16 agree to participate. Because participat ion is voluntary, there is unavoidab le potential for participation bias. To a ssess for this, we will com- pare participating and refusing nursing homes using available data such as size, ownership, and nursin g staff- ing. To avoid long del ays between recruitment a nd par- ticipation, we will recruit in waves of six, six, and four. Once the first six nursing homes (clusters) are re cruited, they will be placed in strata and stripped of all identifiers except a study number. An independent investigator blinded to nursing home name and characteristics wil l assign block size based on the strata size; if only two nursing homes are in a strata block size, two will be used, otherwise block size will be randomly assigned at two, four, or (if applicable) six. The independent investigator will then randomize within blocks into study arms using a ran- dom number generator.https://slowjamsundays.com/wp-content/plugins/formcraft/file-upload/server/content/files/1629ce7baa12e2---Craftsman-dys-4500-manual-pdf.pdf The randomization sequence and block size will be concealed until interventions are assigned. Lists of resi- dents who have fallen duri ng the study period will be generated from the nursing home MDS and incident reports. A random sample of 50 unique residents from each nursing home will be se lected for chart abstraction using a random number generator. Because this is a minimal risk study in which residents are not followed prospectively, we have obtained a waiver of infor med consent. Staff sample Staff members who work with residents in a clinical capacity (e.g., registered nur ses, licensed practice nurses, NAs, social workers, dietary, activities, physical and occupational therapists) on skilled and assisted living units will be eligible to part icipate. The only exclusion criterion is inability to understand English. Using cur- rent staff lists provided by the administrator, we will invite staff to participate. In our pilot studies, 80 to 84 of staff invited, participated in survey completion. Thus, we conser vatively estimate tha t of about 960 staff members, 60 will participat e in training and complete surveys for an estimated enrollment of 576 staff mem- bers. New employees will be invited to participate in CONNECT up to the fourth week of the intervention. Those joining later will be invited to enroll only to com- plete the cross-sectional staff interaction measures. Anderson et al. Implementation Science 2012, 7:11 Page 4 of 14 Risks and challenges A major challenge for nursing home research is the potential for staff turnover. Using a successful strategy from our prior studies, we will secure a written commit- ment from the nursing home administrator, director of nursing, and if relevant, a corporate representative, that the study will continue even if one or more top admin- istrators leave.AYBAR-GALLERY.COM/userfiles/files/Campbell-hausfeld-dh5300-manual.pdf We also have designed this study to be robust to staff turnover by incorporating the CONNECT in-class learning ses sions into the nursing home ’ so r i e n - tation for ne w staff. Exploratory ana lyses will determine whether staff turnover affects the fall-related processes or fall rate measures. Another challenge for nursing home research is designing approaches that are appro- priate and acceptable for all levels of staff, regardless of education and socio-economic background. The interventions CONNECT will be implemented over 12 weeks, fol- lowed by FALLS for an additional 12 weeks. All aspects of the interven - tions are applied to cluster level; even aspects that are delivered to individuals, address cluster level interac- tions. The complete text of the CONNECT and FALLS protocols are available on request to the authors. Design To ensure design fidelity, we standardized the CON- NECT and FALLS protocols to a specified dose in terms of number, frequency, and length of contact. Training CONNECT and FALLS will be delivered by different research interventionists trained separately to minimize contamination. The protocol specifies training content, structured practice, and role-play exercises to ensure that interventionists ’ skills meet established standards. Delivery To ensure that CONNECT and FALLS are delivered as intended, a research team member will observe the interventionists on a random sc hedule, completing stan- dardized checklists. The interventionists and PIs will discuss the r esults and problem-sol ve barriers to adher- ence and repeat concepts and role-play as needed. We will track participants that complete study components. For CONNECT, we will use: contact summary sheets for each visit to a research site; databases for interven- tionists to record co ntacts with participants; and si gn-in sheets to document partic ipation in sessions.https://www.advancedevents.ro/wp-content/plugins/formcraft/file-upload/server/content/files/1629ce7c71d5bc---craftsman-dys-4500-manual-download.pdf For FALLS, we will use: contact sheets to record each con- tact between in terventionists and the Fal l Team; sign-in sheets to document participation in post-fall problem- solving sessions; and databases to track completion of educational modules via requests for continuing educa- tion credit or certificate of completion. Receipt of treatment For CONNECT, participants ’ self-monitoring of local interactions will provide a measure of adherence and behavior change. The clas s sessions will inc lude discus- sion and practice during which skills can be systemati- cally assessed. For FALLS, pa rticipants will complete post-tests in the educational modules. Enactment of skills Researchers will systematically assess enactment when they shadow the in-house facilitators to observe how they practice mentoring behaviors. The researchers will also assess and rec ord enactment by participan ts during structured mentoring. Finally, they will assess enactm ent by observing at least two orie ntation sessions in which the in-house facilitator delivers the in-class session to new employees. Fall risk reduction indictors will be used to measure enactment of the FALLS intervention. In meetings (e.g., nurses meetings, CNA meetings), researchers will explain the study and invite staff to participate in the other aspects of the study (completing surveys, structured mentoring). Staff not Anderson et al. Implementation Science 2012, 7:11 Page 5 of 14 CONNECT Advanced (Session 2). Brief review followed by focus on the more advanced strategies of cognitive diversity, using storytelling, role-playing, and discussion of participants ’ experiences in applying concepts. Interdisciplinary learning facilitates skill acquisition, creation of new horizontal and vertical connections among staff, and learning through cognitive diversity. RNs, LPNs, NAs, social work, activities, rehab, MD, NP; dietary, administration 2, 30 min sessions occurring 2 weeks apart (1.https://webscape.co.bw/wp-content/plugins/formcraft/file-upload/server/content/files/1629ce7ce3f300---Craftsman-dyt-4000-lawn-tractor-manual.pdf0 hrs total) In-House Facilitator Training Protocols In-House Facilitator Class Training. Chance Encounter Mentoring Training. Support by research facilitators. The researcher contacts the in-house facilitators weekly for support and advising; in-house facilitators also have a phone number to call to seek help from research staff as needed. Prepares in-house care and supervisory staff to build trust and maintain consistency of CONNECT with the local culture. Facilitates information exchange between nursing home staff and research staff. In-house facilitators develop self- efficacy in using chance encounters to model local interactions and to mentor staff. Care staff or managers in clinical departments (e.g., nursing, social work, activities). Individuals self-selected with encouragement of study staff. 1, 1 hr learning session; Up to 1 hr of shadowing during regular work activities; 5, 10 min discussions (up to 2 hrs, 50 min total) Relationship Map Protocols Group-to-group maps Session 1. Researcher assists staff to describe actual interactions between work groups (e.g., NAs, LPNs, SW, Dietary, etc.). Session 2. Researcher assists staff to depict new interaction patterns and develop guidelines for improved group-to-group interaction patterns. Assists staff to make interaction patterns explicit (develop a group-to-group relationship map), and agree on guidelines for improved interactions. Assists staff to evaluate relationships. Self- monitoring reinforces and sustains newly acquired behaviors and provides a measure of adherence and behavior change. The researcher uses a semi-structured guide to elicit concerns about using the strategies. All CONNECT participants 2, 10 min sessions (20 min total) Anderson et al. Implementation Science 2012, 7:11 Page 6 of 14 A research team member will answer questi ons and obtain written informed consent.avtomix.com/upload/files/Campbell-hausfeld-dh3800-manual.pdf Everyone completing both learning sessions and staff surveys will receive practical items (water bottles, tote bags) with the study logo. Data collection from staff Data will be collected from enrolled staff at baseline, and at three and six m onths following baseline. Be cause some nursing home staff may have low literacy or Eng- lish as a second language, obtaining reliable data will require special attention; our team has experience col- lecting data from diverse su bjects. To ensure complete and reliable data, we have chosen measures that have been used in nursing homes and are at a sixth grade reading level. To ensure confidentiality, participants can place completed surveys directly in a sec ure drop box in the nursing home. Because surveys will be completed four times, we will change the order of the items each time to reduce the likelihood that respondents will rely on memory of previous responses. Data collection from residents A list of eligible residents who have fallen in the study periods will be obtained from the minimum data set (MDS) nurse or the falls coordinator. We will select a sample of residents via a random number generator for chart abstraction. We have obtained a waiver of Health Insurance Portability and Accountability Act of 1996 authorization and informed consent for resident chart abstraction for the falls-related process measures. Falls data sources and abstraction timing Data sources include MDS, resident medical record, medication administration records, fall or incident logs, and administrat ive facility bed-occupancy r ates. All data sources will be examined over the si x months preceding study initiation and six months following the FALLS intervention. Medical records are retained in the nursing home by law for at least two years after resident dis- charge. Abstractor qualifications, training, and blinding Data abstractors will hold clinical degrees and will be trained using practice charts and a manual including definitions, data locations, and detailed instructions. Instruction will be repeated until inter-rater reliability exceeds 90. Data collectors are employed by CCME and will be blinded to the nursing home ’ si n t e r v e n t i o n status and study hypotheses. Blinding will be assessed by asking data collectors to indicate which study group they believe the nursing home was assigned. Data reliability To ensure data quality, a random 5 of residen t charts at each time period will be abstracted by a second data collector, with inter-rater reliability calculated using kappa. In addition, da ta will be collected about each nursing home, including bed size, nursing staff hours. Chain and religious affiliation will be collected from publicly available sources Nursing staff turnover during the intervention period will be obtained from admin istrators. These data will be used as covariates in the multivariable outcomes ana- lyses. All measures will be aggregated to the cluster level of the facility. Complexity science measures Complexity Science Measures (Table 4) will be collected at time points as indicated. We will ask staff to report their experience over the last month; this time frame was chosen to captur e the usual monthly cycle of meet- ings and events that may influence interactions. They record the number and descriptions of chance encounter mentoring sessions. At least 5 such encounters should occur daily during naturally occurring usual work activities. Identifies staff concerns and barriers, facilitates ongoing learning about interaction, and strengthens sustainability of new behaviors. Facilitators learn to use existing time differently. Implementation Science 2012, 7:11 Page 7 of 14 Topics include 1) staff fall prevention education; 2) medications and falls 3) patient and family fall education; 4) orthostatic hypotension; 5) vision assessment and intervention; 6) gait and balance assessment and intervention 7) environmental assessment and intervention; 8)challenging behavior management; 9) establishing a culture of safety; 10) audit and feedback; and 11) Wrap-up and re-setting goals Reinforces key concepts of multi-factorial risk reduction, supports FALLS Coordinator and maintains enthusiasm. Covers impact, fall risk factor assessment and intervention focusing on orthostatics, gait, toileting, medications, environmental hazards. NA module. Covers fall risk factor identification and intervention focusing on gait, footwear, toileting, hip protectors, and environmental hazards. RNs, LPNs, NAs, MDs, NPs, PAs, Consultant Pharmacists and others (PT, SW, Activities etc) 30-60 min Post-Fall Problem-solving Academic Detailing Nursing home frontline staff is invited to participate in consultations with the researcher and FALLS Coordinator regarding their most challenging residents with falls, modeling risk factor assessment and multi-factorial interventions. Sessions occur at each nursing station during the day and evening shifts. Nurses, NAs, other interested staff 2, 20 min sessions (40 min total) Audit and Feedback Feedback Report Report uses visual (bar graph) and written depictions of the nursing home ’ s current practice on fall-related process and outcome measures, and how this compares with peer nursing homes. Researcher presents and explains the feedback report to FALLS Team. FALLS team, others as desired by Falls Coordinator 30 min Toolbox Morse Fall Scale: Validated scale that quantifies fall risk in nursing home residents; Nurse Fall Risk Reduction Worksheet: Prompts nurse to identify and modify reversible fall risk factors. Provides modifiable tools to assist with communication, implementation, and documentation of multi-factorial risk reduction. FALLS Coordinator determines dissemination Voluntary Anderson et al. Implementation Science 2012, 7:11 Page 8 of 14 Further, we estab- lished adequate reliability at the organizational level using ICC, k, Eta-squared, and alpha coefficients on aggregated items scores. We will calculate the proportion of fallers with medical record evidence of the fall risk reduction indicator, and determine indicator counts for each resident. Timing of the risk factor reduction will be recorded as: within 48 hrs of a fall, within one month of a fall, during the six-month abstraction period. XX Anderson et al. Implementation Science 2012, 7:11 Page 9 of 14 Proportion of repeat fallers and pro- portion of injurious falls (defined as proportion of falls resulting in injury including skin tear, hemat oma, frac- ture, lacerat ion, need for imaging or urgen t assessment) will be measured as secondary fall endpoints. Blinding Because of the nature of the intervention, it is not possi- ble to mask the study assignment from the subjects or the research interventionists. However, the outcomes assessment of falls quality indicators will be completed by independent nurses employed by the state Quality Improvement Organization who will be blinded to study assignment. Success of blinding will be evaluated by ask- ing these nurses to state wh ich intervention they believe that the nursing home received. Analysis The study hypotheses pertain to the cluster level of the nursing homes. Hi erarchical linear modeling with G lim- mix was used to account for clustering in this study. This procedure is useful when there are multi-level, nested sources of variability such as patients and staff clustering within nursing homes. The Glimmix procedure analyzes both individual and group level tra- jectories of change over time. Our hypotheses (H) to address the study aims and related analysis are listed below. As the dependent variables for H1a and H1b are counts, we will use PROC GLIMMIX to estimate the models. A significant negat ive coefficient will indicate that the intervention reduced fall rates. As is standard practice with Poisson models, we will test for over dis- persion in initial analyses and employ a negative-bino- mial model if over dispersion is present. Table 4 Complexity science measures Concept Measured; Source Psychometrics; Calculation Demographics; self-report Age, sex, job title, years in position, education, and ethnicity (collected at baseline or at enrollment into the study. Because the scale has not been used in a nursing home sample previously we tested the reading level and found that it read at the 6th grade level, which is acceptable for this low literacy sample. We revised the wording for nursing homes. Anderson et al. Implementation Science 2012, 7:11 Page 10 of 14 The dependent variab les for H2a and H2b will consist of one Poisson distributed outcome (fall rates), and one dichotomous outcome (the probability of a recurrent fall). For fall rates, we will use PROC GLIMMIX to re- estimate the model with fall rates dependent and inter- vention group, time, and relevant covariates as predic- tors, using the same analysis as for Aim 1. To test H2c, we will add the process measures (from Aim 1) to the models for H2a and H2b as time-changing predictors. For these outcomes, we will use PROC MIXED to estimate a mixed model to estimates the effect of the intervention on each outcome averaged over time. H3B Improvements in fall-rel ated process measures and fall-related out come measures will be mediated by changes in complexity science measures. To test H3B, we will calculate nursing home-level means on the complexity science measures, add these mean as time-changing predictors to our models (above) predicting fall-related pro cess and outcome measures. Hierarchical linear modeling with SAS PROC Glimmix was used to account for clustering in this study. This procedure is useful when there are multi-level, nested sources of variability such as patient and staff clustering within nursing homes. The Glimmix procedure analyzes both individual and group level trajectories of change over time, and can be used to estimate models where persons within clusters are changing over time. Maximum clus- ter size was limited by the pool of resident fallers and number of staff in each facility. The power analysis algo- rithms used to determine cluster size take clustering at the individual level into account. In the analyses, we will treat cluster as a fixed rather than as a random effect. This approach will more adequately control on pot ential confounders at the level of the nursing home. With c l u s t e ra n a l y z e da saf i x e de f f ect, inter-cluster correla- tions are not needed to calculate power. For aim one, we wi ll have 80 power to det ect a 15 difference in risk factor assessment and intervention scores, which is considered to be the minimally clinically significant improvement in f alls care practice. For aim two, the resident sample will provide 80 power to detect a 23 difference in the fall rate due to interven- tion, and a 23 difference in the probability of a recur- rent fall. Because thi s is a real world effectiveness study, this change in fall rate is slightly smaller than that seen in a randomized controlled trial of multifactorial risk factor reduction, but still clinically meaningful. A sw eh a v eas i n g l ep r i m a r yo u t c o m ea n ds e v e r a la d d i - tional outcomes that are exploratory, we will not adjust our significance tests for multiple tests. We expec t 15 attrition on our dependent variables across waves. These models will be operationalized where necessary.