Review your problem or issue (Pressure Ulcer Prevention) and the study materials (Attached) to formulate a PICOT question for your capstone project change proposal. A PICOT question starts with a desi

Marita G. Titler, PhD, FAAN a,⁎, Paul Conlon, Pharm D, JD b, Margaret A. Reynolds, PhD, FAAN b, Robert Ripley, PharmD, BCPS b, Alex Tsodikov, PhD c, Deleise S. Wilson, PhD, RN a, Mary Montie, PhD a aSchool of Nursing, University of Michigan, USAbTrinity Health, Novi, Michigan, USAcDepartment of Biostatistics, School of Public Health, University of Michigan, USA abstract article info Article history:

Received 3 August 2015 Accepted 17 December 2015 Keywords:

Falls Fall injuries Fall prevention Hospitals Implementation Translation Background:Falls are a major public health problem internationally. Many hospitals have implemented fall risk assessment tools, but few have implemented interventions to mitigate patient-specific fall risks. Little research has been done to examine the effect of implementing evidence-based fall prevention interventions to mitigate patient-specific fall risk factors in hospitalized adults.

Objectives:To evaluate the impact of implementing, in 3 U.S. hospitals, evidence-based fall prevention interven- tions targeted to patient-specific fall risk factors (Targeted Risk Factor Fall Prevention Bundle). Fall rates, fall in- jury rates, types of fall injuries and adoption of the Targeted Risk Factor Fall Prevention Bundle were compared prior to and following implementation.

Design:A prospective pre–post implementation cohort design.

Setting:Thirteen adult medical-surgical units from three community hospitals in the Midwest region of the U.S.

Participants:Nurses who were employed at least 20 hours/week, provided direct patient care, and licensed as an RN (n = 157 pre; 140 post); and medical records of patients 21 years of age or older, who received care on the study unit for more than 24 hours during the designated data collection period (n = 390 pre and post).

Methods:A multi-faceted Translating Research Into Practice Intervention was used to implement the Targeted Risk Factor Fall Prevention Bundle composed of evidence-based fall prevention interventions designed to miti- gate patient-specific fall risks. Dependent variables (fall rates, fall injury rates, fall injury type, use of Targeted Risk Factor Fall Prevention Bundle) were collected at baseline, and following completion of the 15 month imple- mentation phase. Nurse questionnaires included the Stage of Adoption Scale, and the Use of Research Findings in Practice Scale to measure adoption of evidence-based fall prevention practices. A Medical Record Abstract Form was used to abstract data about use of targeted risk-specific fall prevention interventions. Number of falls, and number and types of fall injuries were collected for each study unit for 3 months pre- and post- implementation. Data were analyzed using multivariate analysis.

Results:Fall rates declined 22% (p= 0.09). Types of fall injuries changed from major and moderate to minor in- juries. Fall injury rates did not decline. Use of fall prevention interventions improved significantly (pb0.001) for mobility, toileting, cognition, and risk reduction for injury, but did not change for those targeting medications.

Conclusions:Using the Translating Research Into Practice intervention promoted use of many evidence-based fall prevention interventions to mitigate patient-specific fall risk factors in hospitalized adults.

© 2015 Published by Elsevier Inc. 1. Introduction Falls are the most common reported patient safety incident in hospi- tals (Anonymous, 2011; Oliver, 2008a; Rubenstein, 2006), and are a major public health problem internationally (Caldevilla et al., 2013;Higaonna, 2015; Quigley & White, 2013; Shmueli et al., 2014). Up to 30% of falls result in injury including fractures, soft tissue trauma and death (Oliver, 2008a; Rubenstein, 2006). Additional consequences include prolonged hospital stay, discharge to long term care facilities, increased hospital costs, patient anxiety, and loss of confidence in mo- bility and activities of daily living (Boltz et al., 2014; Caldevilla et al., 2013; Oliver et al., 2004; Rubenstein, 2006; Tinetti & Kumar, 2010).

Hospitals have instituted fall risk assessment scales to identify pa- tients at risk for falls followed by implementation of general fall Applied Nursing Research 31 (2016) 52–59 ⁎Corresponding author. Tel.: + 1 734 763 1188.

E-mail address:[email protected](M.G. Titler).

http://dx.doi.org/10.1016/j.apnr.2015.12.004 0897-1897/© 2015 Published by Elsevier Inc. Contents lists available atScienceDirect Applied Nursing Research journal homepage:www.elsevier.com/locate/apnr prevention interventions (e.g., putting signs on the door for those at risk) (Caldevilla et al., 2013; Oliver, 2008b). Although fall prevention in- terventions should be customized to the individual’s identified risk fac- tors (Anonymous, 2011; Cameron et al., 2012), hospitals have not yet promoted use of fall prevention interventions targeted to patient- specific risks (e.g., ambulation or refer to physical therapy for unsteady gait) (Coussement et al., 2008; Hempel et al., 2013; Oliver, Healey, & Haines, 2010). Because falls are complex and risks for falls are multifac- torial, beneficial effects of fall reduction interventions may increase when interventions target patient-specific fall risk factors (Anonymous, 2011; Cameron et al., 2012; Coussement et al., 2008; Tinetti, 2003). Few studies however have examined the effect of implementing evidence-based fall prevention interventions to mitigate patient-specific fall risk factors in hospitalized adults (Dykes et al., 2010).

The purpose of this 18 month study was to implement evidence- based fall prevention interventions targeted to patient-specificfallrisk factors (Targeted Risk Factor Fall Prevention Bundle) and evaluate the impact on reducing falls and fall related injuries. A multifaceted Trans- lating Research Into Practice intervention was used to promote uptake and use of the Targeted Risk Factor Fall Prevention Bundle in 13 adult medical surgical units in three community hospitals in the U.S. Specific aims of the study were to (1) compare fall rates, fall injury rates, and types of fall injuries prior to and following implementation of the evidence-based Targeted Risk Factor Fall Prevention Bundle, and (2) evaluate adoption of the evidence-based Targeted Risk Factor Fall Prevention Bundle.

2. Fall Prevention Conceptual Framework The conceptual framework used in this study was informed by a tax- onomy that classifies types of fall prevention interventions (Cameron et al., 2012; Hook & Winchel, 2006; McCarter-Bayer, Bayer, & Hall, 2005). Interventions are conceptualized as Universal Fall Precautions (e.g., reducing environmental risks for falls such as patient room and hall free of clutter), General Fall Prevention Interventions (e.g., bedside table, call light and other personnel items within reach) and Targeted Individual Risk-Specific Interventions (interventions that target patient-specific fall risk factors).

Individual risk factors that consistently contribute to falls in hospi- talized adults are gait instability and lower limb weakness; urinary in- continence, frequency or need for toileting assistance; previous fall history; agitation/confusion or impaired judgment; and polypharmacy and prescription of“culprit”drugs, in particular centrally acting seda- tives and hypnotics (Oliver et al., 2004; Titler et al., 2011). Mitigating as many of these risk factors as possible is an effective way to reduce falling (Cameron et al., 2012; Oliver, 2008a).

The Targeted Risk Factor Fall Prevention Bundle, developed for this study, focused on interventions that reduce or modify patient-specific fall risk factors as outlined inTable 1. Fall prevention interventions were grouped by categories of risk to address (1) previous falls, (2) mo- bility limitations, (3) elimination, (4) medications, (5) factors that in- crease risk for serious injury from a fall (e.g., anticoagulants), and (6) cognitive and mental status. Some fall prevention interventions such as purposeful rounding are effective to address multiple risk fac- tors (e.g., mobility impairments, elimination, comfort) (Fischer et al., 2005; Woodard, 2009 ). Others such as physical therapy referral, passive and active range of motions, and ambulation are important interven- tions targeted to mobility impairments, a risk factor for falls as well as functional decline (Markey & Brown, 2002; Ross & Morris, 2010; Tucker, Molsberger, & Clark, 2004). Medication management addresses vigi- lance and modification of types and number of medications that pa- tients receive as well as the time of the day they are administered (e.g., diuretics) (Agostini, Concato, & Inouye, 2008; Agostini, Zhang, & Inouye, 2007). Medication reviews with pharmacist are helpful in de- creasing falls (P.P.S. Advisory, 2008).3. Methods A prospective, pre–post cohort implementation design using a partic- ipatory partnership research approach was used for this study. We chose a participatory partnership approach to foster engagement, ownership of thestudy,aswellasuseoffindings to improve quality of care (Cornwall & Jewkes, 1995; Gold & Taylor, 2007; Green, Daniel, & Novick, 2001).

3.1. Setting The study setting was three community hospitals in the Midwest re- gion of the U.S. representing small (90 acute care beds), medium (243 Table 1 Targeted risk factor fall prevention bundle.

Common risk factors⁎ Fall reduction interventions suggested for each risk factor type⁎⁎ Mobility(gait instability, lower limb weakness or required assistance getting out of bed)1. Ambulate 3 to 4 times per day with assistance as needed unless contraindicated.

2. Refer to Physical Therapy for assessment and gait and strength training as needed.

3. Active range of motion three times per day 4. Minimize use of immobilizing equipment (e.g., indwelling urinary catheters, restraints) 5. Assure proper assist equipment (e.g., walker, cane) is readily available and in proper working condition Elimination: Fecal or urinary incontinence(urgency, need for toileting assistance, diuretics)1. Schedule toileting and assistance to the bathroom (e.g., every two hours) 2. Bedside commode available for use 3. Administer diuretics before 5 p.m. to minimize nighttime toileting 4. Look for signs of urinary tract infec- tion and notify physician 5. Stay within arm’s reach during toileting Medications(sedatives, anti- depressants, anticonvulsants, benzodiazepines and polypharmacy)1. Pharmacy review of medications for recommendations 2. Review medications to minimize number.

3. Assist with toileting prior to administration of analgesics unless pain is too severe.

Cognition/mental status(agitation, confusion, disorientation, cognitive impairment,)1. Delirium screen—use a delirium screening tool to assess 2. If possible avoid high risk drugs (e.g., opiates, sedatives, antidepressants) 3. Monitor electrolytes 4. Encourage nighttime sleep—use non- pharmacological mechanismsfirst 5. Encourage use of visual aids (e.g., glasses); use adaptive equipment such as large illuminated telephone key pads, andfluorescent tape on call button with reinforcement to use call button.

6. Assess pain control at regular intervals 7. Rounding every 1–2hours Risk for serious injury from a fall (osteoporosis or osteoporosis risk factors, medications/anticoagulants, postoperative.)1. Indicate as high risk for injury from fall 2. Conduct hour toileting rounds 3. Use low bed (6 inches from thefloor) 4. Consider placing a bedside mat on the floor when patient is in bed.

5. Assist with toileting prior to administration of analgesics.

⁎ Factors repeatedly found as significant risk factors for falls in hospitalized older adults (Oliver et al., 2004; Tinetti & Kumar, 2010).

⁎⁎ Suggested interventions for each risk factor group (Cameron et al., 2012; Cumming, 2002; Dykes et al., 2010; Fischer et al., 2005; Healey et al., 2004; Hook & Winchel, 2006; Inouye, Brown, & Tinetti, 2009; Inouye et al., 2000; Oliver et al., 2004, 2007; Quigley et al., 2009; Ross & Morris, 2010; Shever et al., 2011; Tzeng, 2011).53 M.G. Titler et al. / Applied Nursing Research 31 (2016) 52–59 acute care beds) and large (471 acute care beds) hospitals. Criteria for site inclusion were (1) acute care community hospital, (2) availability of at least one adult medical, surgical or medical-surgical unit, and (3) and interest in reducing falls.

Thirteen adult medical-surgical units from these 3 hospitals were in- vited to participate in the study. Exclusion criteria were critical care, ob- stetrics and pediatric units. Institutional review board (IRB) approval was obtained from the University of Michigan, as well as corresponding review boards at the participating hospitals.

3.2. Subjects Nurses from the study units who met inclusion criteria were ran- domly selected from a list of eligible subjects to receive questionnaires prior to and following implementation of the Targeted Risk Factor Fall Prevention Bundle. Inclusion criteria for nurses were: (1) provided di- rect patient care on the study unit, (2) licensed as a registered nurse (RN), and (3) worked an average of 20 hours or more per week (.50 Full Time Equivalent).

Medical records of a randomly selected group of patients who met the following inclusion criteria were abstracted to determine what targeted, individual risk-specific fall prevention interventions were used for each patient: (1) age≥21 years of age, (2) resided on the study unitN24 hours, and (3) care was received on the study unit during the designated data abstraction period. Medical records were abstracted for each day from admission to discharge from the study unit. Ten records/per unit/month reflecting 3 months of care delivered prior to implementation and 3 months at the end of implementation were abstracted (390 records at baseline; 390 records post-implementation).

3.3. Implementation Intervention A Translating Research Into Practice (TRIP) multifaceted implemen- tation intervention (seeFig. 1) was used to promote uptake and use of the fall prevention bundle, and was guided by the Translation Research Model (Titler, 2010; Titler & Everett, 2001; Titler et al., 2009) developed from Roger’s Diffusion of Innovation Framework (Rogers, 2010). The Translation Research Model addresses four areas that effect adoption of evidence in practice: thecharacteristicsof the evidence-based clinical topic (e.g., the fall prevention bundle), and how it iscommunicatedto usersin thesocial system(e.g., hospital, patient care unit) (Rogers, 2010; Titler et al., 2009).The Translating Research Into Practice intervention, depicted in Fig. 1, consisted of multiple implementation strategies, organized by the Translation Research Model, and informed by prior research (Birken, Lee, & Weiner, 2012; Bradley et al., 2004; Dobbins et al., 2009a, 2009b; Dogherty et al., 2012; Dougherty & Conway, 2008; Farm- er et al., 2008; Feldman & McDonald, 2004; Feldman et al., 2005; Flodgren et al., 2011; Forsetlund et al., 2009; Gifford et al., 2007, 2011; Greenhalgh et al., 2005; Grimshaw et al., 2006; Hysong, Best, & Pugh, 2006; Ivers et al., 2012; Jordan et al., 2009; Katz et al., 2009; Murtaugh et al., 2005; Ploeg et al., 2010; Rogers, 2010; Stetler et al., 2009; Titler, 2010; Titler et al., 2009). It was implemented over 15 months, as outlined inTable 2. To address thesocial system, a 60 minute meeting with the Chief Nursing Officer of each hospital was held at the beginning of implementation to overview the study, select the site opinion leader, and overview fall prevention interventions that mitigate patient- specific fall risk factors. All sites had an institution fall prevention quality improvement committee and the sites were encouraged to incorporate this project into the committee’s work. A 60 minute education program was provided for the nurse managers of the study units to review the study, overview fall prevention interventions to mitigate patient- specific fall risks, and to explain the expectations of the site opinion leader and unit-based change champions. To address the characteristics of the clinical topic, (1) a set of six quick reference guides to assist clini- cians with clinical decision-making were developed and organized by risk factor categories with suggested fall prevention interventions to ad- dress each; and (2) a set of 9 posters were developed about falls, patient-specific fall risk factors and fall prevention interventions to mit- igate these risks. The posters were used in education of staff and posted in key areas on patient care units such as medication rooms and nurses’ stations. To addresscommunication, a nurse opinion leader for each hos- pital and nurse change champion for each of the 13 study units were se- lected in collaboration with the Chief Nursing Officer and Nurse Managers. A two-day train-the-trainer program for the opinion leaders and change champions was held with the overall purpose of increasing their knowledge and skills in fall prevention interventions to mitigate patient-specific fall risk factors, and to develop an implementation ac- tion plan for their respective practice sites. Staff education was done at each site by the opinion leader and change champions using inservices, point of care coaching with staff, and imparting knowledge through unit bulletin boards. Seven outreach visits to each of the hospitals were done by thefirst author in conjunction with the site opinion leader. These visits included rounding on each of the study units to address staff ques- tions, reviewing progress in implementation of the fall prevention inter- ventions, and discussion of strategies to promote use of the fall Characteristics of Clinical Topic:Fall PreventionQRGs Posters CommunicationOpinion Leaders Change Champion Staff Education Outreach Visits Train-the-Trainer Program Social System:Hospital/UnitMeeting with CNO Educational program for nurse managers Adoption: Outcomes & Processes MeasuresFall rates Fall injuries Use of risk-specific fall prevention interventions Users of the EBPPerformance gap assessment Audit and feedback Teleconferences Fig. 1.Translation Research Model. 54M.G. Titler et al. / Applied Nursing Research 31 (2016) 52–59 prevention practices. To address theuserscomponent of the model, we used performance gap assessment, audit and feedback, and monthly teleconferences. Performance gap assessment informs practitioners, at the beginning of change, about a practice performance and opportuni- ties for improvement (Titler et al., 2009). We provided unit fall rates, and types of fall injuries during the train-the-trainer program and used this as a focal point of discussion for improving fall prevention practices. Audit and feedback is ongoing auditing of performance indi- cators (e.g., use of fall prevention practices to mitigate patient-specific risks) and discussing thefindings with practitioners during the practice change (Ivers et al., 2012). The change champions of each unit complet- ed audits offive randomly selected medical records per month to ascer- tain if patient-specific fall risk factors were present and if so, were fall prevention interventions implemented to mitigate the specificriskfac- tors. Completed audits were sent to the project office. Bar graphs were then developed regarding number of patient days with a specific fall risk (e.g., mobility issues) and the number of patient days with imple- mentation of corresponding risk-specific fall prevention interventions (e.g., physical therapy referral; ambulation). These were discussed with staff during each of the outreach visits. Monthly 60 minute tele- conferences among the opinion leaders and change champions from the study units were conducted to share implementation strategies and problem-solve issues that arose.

3.4. Dependent Variables and Instruments The dependent variables for aim 1 were: (1) fall rates; (2) fall injury rates; and (3) types of fall injuries. A fall was defined as an unplanned descent to thefloor, and was calculated, at the unit level, by the number of inpatient falls multiplied by 1000 and divided by the total number of inpatient days (Anonymous, 2004). Types of fall injuries were defined as minor (needs application of dressing, ice, cleaning of a wound, limb elevation, topical medication), moderate (results in suturing, steri- strips, fracture, or splinting), major (results in surgery, casting, or trac- tion), and death (as a result of the fall) (Anonymous, 2004). A fall injury empirical rate was calculated by multiplying the number of inpatient falls with injuries by 1000 and dividing by total number of inpatient days (Anonymous, 2004).

The variables for aim 2 were: (1) number and type of risk-specificin- terventions (e.g., ambulation with assistance; physical therapy referral) delivered within and across each risk category (e.g., mobility); and (2) RNs level of adoption of the Targeted Risk Factor Fall Prevention Bundle. A Medical Record Abstract Form and detailed directions were designed to abstract data from medical records about use of fall preven- tion interventions that targeted patient-specific fall risk for each pa- tient/medical record. Medical record abstraction is used by other investigators and the Center for Medicare and Medicaid Services to col- lect data on processes of care (Centers for Medicare & Medicaid Ser- vices: CMS.gov., 2014; Titler et al., 2009). The Medical Record Abstract Form was reviewed by 3 experts in fall prevention and then tested with 20 medical records for completeness and clarity. The MedicalRecord Abstract Form and detailed instructions were then tested for inter-rater and intra-rater reliability using 30 medical records resulting in reliability of .92 and .95, respectively. Medical record data were used to calculate the number and type of risk-specificinterventions (e.g., ambulation with assistance; physical therapy referral) delivered within and across each risk category (e.g., mobility; elimination) by number of patient days.

The RNs level of adoption of the targeted fall prevention interven- tions was measured by theStage of Adoption Instrumentand the Use of Research Findings in Practice Scale. The Stage of Adoption Instrument is based on Rogers’(Rogers, 2010) diffusion of innovation model, and measures stages of awareness, persuasion, and implementation for four evidence-based, fall prevention practices that target individual risk factors: ambulation; medication review; delirium screening; and post-fall huddle. It is scored on a 0 (low stage of adoption) to 4 (imple- mentation) scale. Internal consistency is .95 to .76 with test–retest reli- ability (one week) of .83 (Rutledge & Donaldson, 1995; Rutledge et al., 1996; Titler et al., 2009). The Use of Research Findings in Practice Scale was used to measure nurses self-reported use of researchfindings for targeted risk factor fall prevention interventions (knowledge to im- plementation). Content validity and test–retest reliability (r = .87) have been demonstrated (Meyer & Goes, 1988; Titler et al., 2009). Nurses rate on a 9-point, Guttman type scale, the extent that they use evidence- based fall prevention interventions targeted to patient-specific risk fac- tors to prevent or reduce falls. Each individual receives a score of 0–9 with lower scores reflecting knowledge awareness stage of adoption and higher scores reflecting implementation/use (Meyer & Goes, 1988; Titler et al., 2009).

3.5. Data Collection Procedures The number of falls, number of injuries from falls, type of injuries from falls, and number of patient days per month were sent to the pro- ject director via secure electronicfiles for each of the study units for a 3 month period at baseline, midpoint and at the end of the implementa- tion phase.

Medical record abstractions were conducted for care delivered 3 months prior to implementation (baseline) and at end of the imple- mentation phase. The project director received a list of all eligible sub- jects/medical records from each study unit for each data collection period (baseline; post-implementation). Ten medical records per month were randomly selected for each study unit, using SPSS (SPSS, 1999) to generate a random sequence of medical records for each data collection period, and the list of eligible records were sent to the medical records department of each site. The trained research assistants were then provided access to the randomly selected medical records, and after confirming inclusion criteria of each record, data were abstracted using the Medical Record Abstract Form.

A list of nurses who met the inclusion criteria for each study unit was sent to the project director by the designated human resource person at each site. Thirty nurses per unit were randomly selected from this list. Table 2 TRIP intervention.

TRIP intervention implementation strategies Translation research model component Time during implementation phase of the study Meeting with CNO and education of nurse managers of study units Social system Month 1 Selection of opinion leaders and unit-based change champions Communication Month 1 Train-the trainer program Communication Month 1 Performance Gap Assessment Users of the EBP Month 1 Quick reference guides Characteristics of the clinical topic Distributed in month 4 Used months 4 through 15 Educational posters Characteristics of the clinical topic Distributed month 3 Used months 3 through 11 Staff education Communication Month 3 to 4 Outreach/site visits every 6 weeks for a total of 7 visits per site Communication Month 4 through month 15 Audit and feedback—6 reports Users of the EBP Month 5 through month 15 Monthly teleconferences total of 11 Users of the EBP Month 3 through 1455 M.G. Titler et al. / Applied Nursing Research 31 (2016) 52–59 For those units that did not have 30 eligible nurses, all that met inclusion were included in the sample (n = 336). Selected RNs were invited to complete a questionnaire consisting of the Stage of Adoption Instru- ment, the Use of Research Findings in Practice Scale, and demographic questions. At baseline (one month prior to implementation), a cover let- ter, the questionnaire, and a preaddressed, stamped, envelope were mailed to each selected nurse. The cover letter explained the study, in- vited the nurse to participate, and requested that the questionnaire be returned within 2 weeks. Return of the questionnaire signified consent to participate. Reminders were mailed to nurses who had not returned the questionnaire within 3 weeks. Post-implementation questionnaires were sent to RNs returning baseline questionnaires, with 1 reminder sent if questionnaires were not returned within 3 weeks of thefirst mailing. The return rate of questionnaires was 47% at baseline (n = 157) and 42% at follow-up (n = 140). Returned questionnaires were en- tered into a database and reviewed for accuracy by a trained research assistant. Unit characteristics (e.g., bed capacity, average daily census, RN skill mix) were collected from nurse managers of the study unit. Patient characteristics (age, severity of illness) were acquired from the inpa- tient discharge abstractfiles (Balas et al., 2004; Titler et al., 2009; Vaughn et al., 2002). The All Patient Refined Diagnosis Related Groups (APR-DRG), the extent of physiological decompensation or organ sys- tem loss of function, was used to define severity of illness (1 = minor; 2 = moderate; 3 = major; 4 = severe) (All Patient DRG Definitions Manual, 2011).

3.6. Statistical Analyses Data werefirst analyzed using descriptive statistics (e.g., mean, stan- dard deviation, counts, percent) and examined for values out of range. A significance level (alpha) ofb.05 was set a priori for all analyses.

For aim 1, multivariate analysis of fall rates was conducted using a Poisson regression model treating counts of falls and fall injuries as a re- sponse variable and the period of observation (baseline and post- implementation), as well as unit-level characteristics as explanatory variables. The model used an off-set term representing log-person- days of observation (e.g., patient days) contributing to the fall counts.

Covariates included patient age, severity of illness (Minor, Moderate, Major, Severe, scored as 1–4, respectively), RN skill mix, and RN HPPD.

Falls and fall injuries were used as dependent variables. Given the small numbers for type of injury, descriptive statistics were used to il- lustrate the number in each category (minor, moderate, major, death), from pre- to post-implementation.

To analyze data about risk-specific fall prevention interventions abstracted from medical records (aim 2), a set of appropriate interven- tions was identified for every type of fall risk. This resulted in the creation of risk-intervention pairs, where a specific risk profile corresponds to a specific fall prevention intervention to mitigate the fall risk. In addition, fall prevention interventions were analyzed by risk categories (e.g., mobility, elimination). For all patient days when a specific risk for falls was present, the presence of a fall preven- tion intervention targeted to a risk factor was treated as a correct response. Multivariate analyses of rates of correct interventions were conducted using a Poisson regression model using the count of correct interventions as the response variable and the period of observation (time before or after implementation), as well as unit- level characteristics (e.g., patient age, severity of illness, RN skill mix) as explanatory variables.

To analyze nurses’self-reported level of adoption (aim 2), multivar- iate analysis was conducted using a linear mixed model with a random intercept modeling the effect of the unit. The model takes into account the fact that nurses from the same unit may have unmeasured charac- teristics that make their responses to the questionnaire within the unit more alike than across different units. Multivariate linear mixed model analyses were run separately for each response variable(e.g., ambulation, medication review, Use of Research Findings in Prac- tice) to evaluate the effect of time (before versus after implementation).

Covariates included years of work experience as an RN, education, and age. Gender and race were excluded, because the overwhelming major- ity was white and female.

4. Results Demographics of the nurses and study units did not differ signifi- cantly between pre- and post-implementation. Patients on the study units were 65 years of age or older ( X= 65.6; SD = 2.8), and the major- ity (68%) were in the moderate or major severity of illness category. The average RN skill mix was 75% and the mean RN hours per patient days was 6.8 (SD = 0.81). The majority of nurses were white (N90%), female (N90%), between 30 and 40 years of age, and with an average of over eight years work experience as an RN. Fifty-five percent of the nurses had an education of a baccalaureate degree or higher.

4.1. Impact on Falls (Aim 1) As shown inTable 3, the decline in fall rates from pre- ( X= 3.69; SD = 1.43) to post- implementation ( X= 2.7; SD = 1.34) was not sta- tistically significant (−0.251 on the log scale; SE = 0.15), but demon- strated a trend toward significance (p= 0.09) with a 22% decline in fall rates. Severity of illness and RN skill mix were significant. For exam- ple, a 1% increase in RN skill mix was associated with a 2.3% decrease in fall rates. Since injuries from falls are a rare event, their analysis carries less power compared to the overall analysis of fall rates. The decline in fall injury rates was not significant (pre: X= .70 SD = .71; post: X= .59 SD = .5;p= 0.73). Age was positively associated with fall injury rates (p= 0.046). A one year increase in age was associated with a 12% increase in injury from a fall. Although fall injury rates did not de- cline significantly, there were reductions in the severity of fall injury for major and moderate categories from 26% pre-implementation to 11% post-implementation (see Fig. 2 supplemental).

4.2. Adoption of the Targeted Risk Factor Fall Prevention Bundle (Aim 2) Analysis of medical record data indicating adoption of fall preven- tion interventions targeting patient-specific fall risk factors showed sig- nificant improvements (pb0.001) from pre- to post-implementation indicating that fall prevention interventions were implemented to ad- dress patient-specific fall risk factors for all risk categories except med- ications (seeTable 4). For example, prior to implementation, there were 1285 patient days when patients had some mobility fall risk factor; fall prevention interventions to mitigate mobility risk were implemented 31 times per 100 patient days. In contrast, post-implementation, there were 1333 patient days when patients had some mobility fall risk factor Table 3 Multivariate analysis of fall and fall injury rates.

Variables/category Estimates of coefficient in the model Std. ErrorpValue Fall rates Intercept−4.14619 1.49725 0.005 Time period (after)−0.251 0.15115 0.09 Patient age−0.01369 0.02187 0.53 Severity of illness 0.41152 0.20609 0.045 RN skill mix 2.29978 0.85774 0.007 RN HPPDs 0.01780 0.04811 0.711 Fall injury rates Intercept−15.0001 3.7964b0.001 Time period (after) 0.1118 0.3207 0.727 Patient age 0.1144 0.0574 0.046 Severity of illness 0.3323 0.5335 0.533 RN skill mix−0.3864 1.7102 0.821 RN HPPDs−0.0624 0.1100 0.571 56M.G. Titler et al. / Applied Nursing Research 31 (2016) 52–59 with a fall prevention intervention implemented 88 times per 100 patient days.

Specific types of fall prevention interventions implemented within each risk category as well as the sum of correct interventions by catego- ry are reported in Table 5 (supplemental materials). Specifictypesofin- terventions with counts greater than 5 were used in this analysis. For example, use of fall prevention interventions targeted to mobility risk increased for physical therapy, use of assistive equipment, and periodic rounding, as well as the overall sum of fall prevention interventions in the mobility risk category. Overall, the rate of correct interventions across risk categories increased by .27 per 100 patient days (SE = 0.4, pb0.001) (see Table 5 supplemental).

Nurses’mean adoption scores increased significantly for:ambulation from 2.03 (1.2) pre-implementation to 2.54 (1.0) post-implementation (SE = .1405;p= 0.002);post-fall huddlesfrom 2.81 (1.2) pre- implementation to 3.1 (1.0) post-implementation (SE = 0.1375;p= 0.03); andUse of Research Findings in Practicescores from 6.4 (2.9) pre-implementation to 7.4 (2.3) post-implementation (SE = .3345; p= 0.04). No covariates were significant, and adoption scores did not change significantly for medication review and delirium screening.

5. Discussion The Translating Research Into Practice intervention used in this study was effective in promoting use of fall prevention interventions that target patient-specific fall risk factors. Increased use included fall prevention interventions for history of previous falls, mobility, elimina- tion, cognition/mental status, and risk for injury from a fall. Targeted risk factor fall prevention interventions were designed to mitigate patient-specific risks factors for falls, which goes beyond general fall prevention interventions typically implemented for those designated at risk (Coussement et al., 2008; Hempel et al., 2013; Oliver, 2008a). Al- though investigators have demonstrated the effectiveness of translation interventions to improve use of evidence-based practices for a variety of other clinical topics (Birken et al., 2012; Dobbins et al., 2009a, 2009b; Dogherty et al., 2012; Farmer et al., 2008; Flodgren et al., 2011; Forsetlund et al., 2009; Gifford et al., 2007, 2011; Ivers et al., 2012; Jordan et al., 2009; Katz et al., 2009; Ploeg et al., 2010; Stetler et al., 2009), few studies have demonstrated improved uptake and use of fall prevention interventions that target patient-specific fall risk factors through a translation research approach (Dykes et al., 2010).

This study demonstrated a 22% reduction in fall rates from pre- to post-implementation with changes in types of injuries from major and moderate to minor injuries. Although the reduction in fall rates and change in types of fall injuries were not statistically significant, these changes are clinically meaningful (Coussement et al., 2008; Cumming et al., 2008; Dykes et al., 2010; Koh et al., 2009). Thesefindings support those of Dykes and colleagues (Dykes et al., 2010) that demonstrated the effectiveness of targeted interventions for reducing falls.Fall rates were associated with severity of illness and patient age.

Thesefindings are congruent with those of other investigators that sug- gest that falls and injuries from falls are influenced by patient age as well as the number and severity of other conditions (Hanlon et al., 2002; Lee, Cigolle, & Blaum, 2009 ).

Our study did not demonstrate statistically significant improvements in use of some fall prevention interventions, particularly those targeted to medications such as pharmacy review of medications, avoiding use of medications that increase fall risk (e.g., benzodiazepines), and toileting prior to administration of analgesics. Based upon nurses’self- report and medical record data,findings did not demonstrate increased use of specific fall prevention interventions related to mental status risk such as physician consultation for mental status changes, and sched- uled rounding. The explanations for failing to see an increased use of these interventions may be due to complexity and the interdisciplinary nature of these fall prevention interventions (Quigley & White, 2013).

Medication review requires multidisciplinary practices (e.g., pharmacy and physicians) and implementation by nurses alone may be difficult.

Even though our study included pharmacy representatives to facilitate communication, it might not have been enough to support medication review. Although cognitive status is a fall risk factor that has been em- phasized (Inouye et al., 1999), implementing interventions to screen and address cognitive status of hospitalized patients is complex and re- mains a challenge noted by others (Bradley et al., 2006; Carroll, Dykes, & Hurley, 2012).

Ambulation is an intervention that is recommended to prevent falls related to mobility limitations (Boltz et al., 2014; Oliver et al., 2004; Tinetti & Kumar, 2010). Nurses’self-reported use of ambulation in- creased from“persuaded”to“used sometimes”stage of adoption. Data from medical records also demonstrated an improvement in use of am- bulation, although this change was not statistically significant (p= 0.08). Thesefindings are similar to those of other investigators who have demonstrated that ambulation is one of several missed nursing care interventions (Kalisch, 2006; Kalisch, Landstrom, & Hinshaw, 2009). Boltz and colleagues (Boltz et al., 2014) demonstrated several factors that patients report as limiting mobility and physical activity during hospitalization, including elevated bed height, lack of assistive devices, and lack of available staff. Hospital leaders and staff must prior- itize and value ambulation and physical activity interventions to pre- vent falls and functional decline.

6. Limitations The studyfindings are not generalizable to other types of healthcare settings, such as ambulatory and long-term care agencies. Given the pre–post design of the study, it is difficult to rule out effects from other factors in the environment occurring simultaneously with this study. Lastly, although medical records are used by regulatory agencies Table 4 Fall prevention interventions by risk categories.

Before implementation (n = 1638 patient days)CI After implementation (n = 1606 patient days)CIpValue Risk-specific interventions ⱡ Patient days + Rate per 100 patient days ¥ Patient days Rate per 100 patient days History of previous fall 133 0.6 0–1.8 209.7 82 77–87b0.001 Mobility 1285 31 29–33 1333 88 87–90b0.001 Toileting/elimination 853.7 50 47–53 917.7 66 63–69b0.001 Cognition/mental status 769 2.3 1.3–3.2 531 77 74–80b0.001 Medication 1525 0.11 0–0.25 1562 0.1 0–0.25 0.981 Risk for injury 1142 66 64–69 1285 88 86–89b0.001 CI—confidence interval.

N = 1638 total patient days before intervention; N = 1606 total patient days after intervention. +Patient days are the number of days of labeled risk (denominator).¥Number of times intervention(s) was received per 100 patient days (example: received a mobility intervention 88 times per 100 patient days).ⱡReceived fall prevention interventions targeted to patient-specific risk (e.g., based on risk profile).57 M.G. Titler et al. / Applied Nursing Research 31 (2016) 52–59 to measure care delivery, some of the fall prevention interventions may have been implemented, but not documented.

7. Conclusions The Translating Research Into Practice intervention improved use of fall prevention interventions targeted to patient-specific fall risk factors.

The study also demonstrated improvement in reduction of fall rates and types of fall injuries. To make significant gains in reducing falls in hospi- tals, clinicians must do more than arriving at a fall risk score with subse- quent implementation of general fall reduction interventions; they need to know each patient’s risk factors for falls and implement fall pre- vention interventions to mitigate those risks.

Appendix A. Supplementary data Supplementary data to this article can be found online athttp://dx.

doi.org/10.1016/j.apnr.2015.12.004.

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