A Randomized Trial of a Multifactorial Strategy to Prevent Serious Fall Injuries.

BACKGROUND: Injuries from falls are major contributors to complications and death in older adults. Despite evidence from efficacy trials that many falls can be prevented, rates of falls resulting in injury have not declined. METHODS: We conducted a pragmatic, cluster-randomized trial to evaluate the effectiveness of a multifactorial intervention that included risk assessment and individualized plans, administered by specially trained nurses, to prevent fall injuries. A total of 86 primary care practices across 10 health care systems were randomly assigned to the intervention or to enhanced usual care (the control) (43 practices each). The participants were community-dwelling adults, 70 years of age or older, who were at increased risk for fall injuries. The primary outcome, assessed in a time-to-event analysis, was the first serious fall injury, adjudicated with the use of participant report, electronic health records, and claims data. We hypothesized that the event rate would be lower by 20% in the intervention group than in the control group. RESULTS: The demographic and baseline characteristics of the participants were similar in the intervention group (2802 participants) and the control group (2649 participants); the mean age was 80 years, and 62.0% of the participants were women. The rate of a first adjudicated serious fall injury did not differ significantly between the groups, as assessed in a time-to-first-event analysis (events per 100 person-years of follow-up, 4.9 in the intervention group and 5.3 in the control group; hazard ratio, 0.92; 95% confidence interval [CI], 0.80 to 1.06; P = 0.25). The rate of a first participant-reported fall injury was 25.6 events per 100 person-years of follow-up in the intervention group and 28.6 events per 100 person-years of follow-up in the control group (hazard ratio, 0.90; 95% CI, 0.83 to 0.99; P = 0.004). The rates of hospitalization or death were similar in the two groups. CONCLUSIONS: A multifactorial intervention, administered by nurses, did not result in a significantly lower rate of a first adjudicated serious fall injury than enhanced usual care. (Funded by the Patient-Centered Outcomes Research Institute and others; STRIDE ClinicalTrials.gov number, NCT02475850.).

A resilience perspective on healthcare personnels' experiences of managing the COVID-19 pandemic: a qualitative study in Norwegian nursing homes and come care services.

BACKGROUND: The COVID-19 pandemic led to new and unfamiliar changes in healthcare services globally. Most COVID-19 patients were cared for in primary healthcare services, demanding major adjustments and adaptations in care delivery. Research addressing how rural primary healthcare services coped during the COVID-19 pandemic, and the possible learning potential originating from the pandemic is limited. The aim of this study was to assess how primary healthcare personnel (PHCP) working in rural areas experienced the work situation during the COVID-19 outbreak, and how adaptations to changes induced by the pandemic were handled in nursing homes and home care services. METHOD: This study was conducted as an explorative qualitative study. Four municipalities with affiliated nursing homes and homecare services were included in the study. We conducted focus group interviews with primary healthcare personnel working in rural nursing homes and homecare services in western Norway. The included PHCP were 16 nurses, 7 assistant nurses and 2 assistants. Interviews were audio recorded, transcribed and analyzed using thematic analysis. RESULTS: The analysis resulted in three main themes and 16 subthemes describing PHCP experience of the work situation during the COVID-19 pandemic, and how they adapted to the changes and challenges induced by the pandemic. The main themes were: "PHCP demonstrated high adaptive capacity while being put to the test", "Adapting to organizational measures, with varying degree of success" and "Safeguarding the patient's safety and quality of care, but at certain costs". CONCLUSION: This study demonstrated PHCPs major adaptive capacity in response to the challenges and changes induced by the covid-19 pandemic, while working under varying organizational conditions. Many adaptations where long-term solutions improving healthcare delivery, others where short-term solutions forced by inadequate management, governance, or a lack of leadership. Overall, the findings demonstrated the need for all parts of the system to engage in building resilient healthcare services. More research investigating this learning potential, particularly in primary healthcare services, is needed.

Derivation of a Clinical Risk Score to Predict 14-Day Occurrence of Hypoxia, ICU Admission, and Death Among Patients with Coronavirus Disease 2019.

BACKGROUND: Uncertainty surrounding COVID-19 regarding rapid progression to acute respiratory distress syndrome and unusual clinical characteristics make discharge from a monitored setting challenging. A clinical risk score to predict 14-day occurrence of hypoxia, ICU admission, and death is unavailable. OBJECTIVE: Derive and validate a risk score to predict suitability for discharge from a monitored setting among an early cohort of patients with COVID-19. DESIGN: Model derivation and validation in a retrospective cohort. We built a manual forward stepwise logistic regression model to identify variables associated with suitability for discharge and assigned points to each variable. Event-free patients were included after at least 14 days of follow-up. PARTICIPANTS: All adult patients with a COVID-19 diagnosis between March 1, 2020, and April 12, 2020, in 10 hospitals in Massachusetts, USA. MAIN MEASURES: Fourteen-day composite predicting hypoxia, ICU admission, and death. We calculated a risk score for each patient as a predictor of suitability for discharge evaluated by area under the curve. KEY RESULTS: Of 2059 patients with COVID-19, 1326 met inclusion. The 1014-patient training cohort had a mean age of 58 years, was 56% female, and 65% had at least one comorbidity. A total of 255 (25%) patients were suitable for discharge. Variables associated with suitability for discharge were age, oxygen saturation, and albumin level, yielding a risk score between 0 and 55. At a cut point of 30, the score had a sensitivity of 83% and specificity of 82%. The respective c-statistic for the derivation and validation cohorts were 0.8939 (95% CI, 0.8687 to 0.9192) and 0.8685 (95% CI, 0.8095 to 0.9275). The score performed similarly for inpatients and emergency department patients. CONCLUSIONS: A 3-item risk score for patients with COVID-19 consisting of age, oxygen saturation, and an acute phase reactant (albumin) using point of care data predicts suitability for discharge and may optimize scarce resources.

Using Electronic Health Record Portals to Improve Patient Engagement: Research Priorities and Best Practices.

Ninety percent of health care systems now offer patient portals to access electronic health records (EHRs) in the United States, but only 15% to 30% of patients use these platforms. Using PubMed, the authors identified 53 studies published from September 2013 to June 2019 that informed best practices and priorities for future research on patient engagement with EHR data through patient portals, These studies mostly involved outpatient settings and fell into 3 major categories: interventions to increase use of patient portals, usability testing of portal interfaces, and documentation of patient and clinician barriers to portal use. Interventions that used one-on-one patient training were associated with the highest portal use. Patients with limited health or digital literacy faced challenges to portal use. Clinicians reported a lack of workflows to support patient use of portals in routine practice. These studies suggest that achieving higher rates of patient engagement through EHR portals will require paying more attention to the needs of diverse patients and systematically measuring usability as well as scope of content. Future work should incorporate implementation science approaches and directly address the key role of clinicians and staff in promoting portal use.

Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study.

BACKGROUND: Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. OBJECTIVE: This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. METHODS: This study was conducted in an academic tertiary hospital in Seoul, Republic of Korea. The hospital had approximately 2000 inpatient beds and 120 ICU beds. As of January 2019, the hospital had approximately 9000 outpatients on a daily basis. The number of annual ICU admissions was approximately 10,000. We conducted a retrospective study between January 1, 2010, and December 31, 2018. A total of 6914 extubation cases were included. We developed a UE prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model's performance, we used the area under the receiver operating characteristic curve (AUROC). The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were also determined for each model. For performance evaluation, we also used a calibration curve, the Brier score, and the integrated calibration index (ICI) to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve. RESULTS: Among the 6914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was higher during the night shift as compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality were higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.763, and for SVM was 0.740. CONCLUSIONS: We successfully developed and validated machine learning-based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787 and the sensitivity was 0.949, which was obtained using the RF algorithm. The RF model was well-calibrated, and the Brier score and ICI were 0.129 and 0.048, respectively. The proposed prediction model uses widely available variables to limit the additional workload on the clinician. Further, this evaluation suggests that the model holds potential for clinical usefulness.

Nursing Documentation Variation Across Different Medical Facilities Within an Integrated Healthcare System.

The purpose of this study was to demonstrate nursing documentation variation based on electronic health record design and its relationship with different levels of care by reviewing how various flowsheet measures, within the same electronic health record across an integrated healthcare system, are documented in different types of medical facilities. Flowsheet data with information on patients who were admitted to academic medical centers, community hospitals, and rehabilitation centers were used to calculate the frequency of flowsheet entries documented. We then compared the distinct flowsheet measures documented in five flowsheet templates across the different facilities. We observed that each type of healthcare facility appeared to have distinct clinical care foci and flowsheet measures documented differed within the same template based on facility type. Designing flowsheets tailored to study settings can meet the needs of end users and increase documentation efficiency by reducing time spent on unrelated flowsheet measures. Furthermore, this process can save nurses time for direct patient care.

Adopting the Fall Tailoring Interventions for Patient Safety (TIPS) Program to Engage Older Adults in Fall Prevention in a Nursing Home.

BACKGROUND: Falls are the leading cause of injury-related hospitalizations and deaths among older adults globally. LOCAL PROBLEM: About 24% of Canadian nursing home residents fall annually. This quality improvement project evaluated the impact of the Fall Tailoring Interventions for Patient Safety (TIPS) program on preventing falls and fall-related injuries among older adult nursing home residents in a subacute care unit in Canada. METHODS: We used the Standards for Quality Improvement Reporting Excellence (SQUIRE) 2.0 guidelines for reporting. The intervention site is a 15-bed subacute care unit within a government-funded nursing home. INTERVENTION: The Fall TIPS program was adapted to a nursing home setting to prevent falls. It provides fall prevention clinical decision support at the bedside. RESULTS: The rates of falls and injuries decreased after implementing the Fall TIPS intervention. CONCLUSION: Engaging nursing home older adult residents in fall prevention is crucial in translating evidence-based fall prevention care into clinical practice.

Development and validation of early warning score system: A systematic literature review.

OBJECTIVES: This review aims to: 1) evaluate the quality of model reporting, 2) provide an overview of methodology for developing and validating Early Warning Score Systems (EWSs) for adult patients in acute care settings, and 3) highlight the strengths and limitations of the methodologies, as well as identify future directions for EWS derivation and validation studies. METHODOLOGY: A systematic search was conducted in PubMed, Cochrane Library, and CINAHL. Only peer reviewed articles and clinical guidelines regarding developing and validating EWSs for adult patients in acute care settings were included. 615 articles were extracted and reviewed by five of the authors. Selected studies were evaluated based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. The studies were analyzed according to their study design, predictor selection, outcome measurement, methodology of modeling, and validation strategy. RESULTS: A total of 29 articles were included in the final analysis. Twenty-six articles reported on the development and validation of a new EWS, while three reported on validation and model modification. Only eight studies met more than 75% of the items in the TRIPOD checklist. Three major techniques were utilized among the studies to inform their predictive algorithms: 1) clinical-consensus models (n = 6), 2) regression models (n = 15), and 3) tree models (n = 5). The number of predictors included in the EWSs varied from 3 to 72 with a median of seven. Twenty-eight models included vital signs, while 11 included lab data. Pulse oximetry, mental status, and other variables extracted from electronic health records (EHRs) were among other frequently used predictors. In-hospital mortality, unplanned transfer to the intensive care unit (ICU), and cardiac arrest were commonly used clinical outcomes. Twenty-eight studies conducted a form of model validation either within the study or against other widely-used EWSs. Only three studies validated their model using an external database separate from the derived database. CONCLUSION: This literature review demonstrates that the characteristics of the cohort, predictors, and outcome selection, as well as the metrics for model validation, vary greatly across EWS studies. There is no consensus on the optimal strategy for developing such algorithms since data-driven models with acceptable predictive accuracy are often site-specific. A standardized checklist for clinical prediction model reporting exists, but few studies have included reporting aligned with it in their publications. Data-driven models are subjected to biases in the use of EHR data, thus it is particularly important to provide detailed study protocols and acknowledge, leverage, or reduce potential biases of the data used for EWS development to improve transparency and generalizability.

Screening for Adverse Drug Events: a Randomized Trial of Automated Calls Coupled with Phone-Based Pharmacist Counseling.

BACKGROUND: Medication adverse events are important and common yet are often not identified by clinicians. We evaluated an automated telephone surveillance system coupled with transfer to a live pharmacist to screen potentially drug-related symptoms after newly starting medications for four common primary care conditions: hypertension, diabetes, depression, and insomnia. METHODS: Cluster randomized trial with automated calls to eligible patients at 1 and 4 months after starting target drugs from intervention primary care clinics compared to propensity-matched patients from control clinics. Primary and secondary outcomes were physician documentation of any adverse effects associated with newly prescribed target medication, and whether the medication was discontinued and, if yes, whether the reason for stopping was an adverse effect. RESULTS: Of 4876 eligible intervention clinic patients who were contacted using automated calls, 776 (15.1%) responded and participated in the automated call. Based on positive symptom responses or request to speak to a pharmacist, 320 patients were transferred to the pharmacist and discussed 1021 potentially drug-related symptoms. Of these, 188 (18.5%) were assessed as probably and 479 (47.1%) as possibly related to the medication. Compared to a propensity-matched cohort of control clinic patients, intervention patients were significantly more likely to have adverse effects documented in the medical record by a physician (277 vs. 164 adverse effects, p < 0.0001, and 177 vs. 122 patients discontinued with documented adverse effects, p < 0.0001). DISCUSSION: Systematic automated telephone outreach monitoring coupled with real-time phone referral to a pharmacist identified a substantial number of previously unidentified potentially drug-related symptoms, many of which were validated as probably or possibly related to the drug by the pharmacist or their physicians. Multiple challenges were encountered using the interactive voice response (IVR) automated calling system, suggesting that other approaches may need to be considered and evaluated. TRIAL REGISTRATION: ClinicalTrials.gov : NCT02087293.

Novel Approach to Inpatient Fall Risk Prediction and Its Cross-Site Validation Using Time-Variant Data.

BACKGROUND: Electronic medical records (EMRs) contain a considerable amount of information about patients. The rapid adoption of EMRs and the integration of nursing data into clinical repositories have made large quantities of clinical data available for both clinical practice and research. OBJECTIVE: In this study, we aimed to investigate whether readily available longitudinal EMR data including nursing records could be utilized to compute the risk of inpatient falls and to assess their accuracy compared with existing fall risk assessment tools. METHODS: We used 2 study cohorts from 2 tertiary hospitals, located near Seoul, South Korea, with different EMR systems. The modeling cohort included 14,307 admissions (122,179 hospital days), and the validation cohort comprised 21,172 admissions (175,592 hospital days) from each of 6 nursing units. A probabilistic Bayesian network model was used, and patient data were divided into windows with a length of 24 hours. In addition, data on existing fall risk assessment tools, nursing processes, Korean Patient Classification System groups, and medications and administration data were used as model parameters. Model evaluation metrics were averaged using 10-fold cross-validation. RESULTS: The initial model showed an error rate of 11.7% and a spherical payoff of 0.91 with a c-statistic of 0.96, which represent far superior performance compared with that for the existing fall risk assessment tool (c-statistic=0.69). The cross-site validation revealed an error rate of 4.87% and a spherical payoff of 0.96 with a c-statistic of 0.99 compared with a c-statistic of 0.65 for the existing fall risk assessment tool. The calibration curves for the model displayed more reliable results than those for the fall risk assessment tools alone. In addition, nursing intervention data showed potential contributions to reducing the variance in the fall rate as did the risk factors of individual patients. CONCLUSIONS: A risk prediction model that considers longitudinal EMR data including nursing interventions can improve the ability to identify individual patients likely to fall.

Acute Care Patient Portal Intervention: Portal Use and Patient Activation.

BACKGROUND: Patient-facing health information technology (HIT) tools, such as patient portals, are recognized as a potential mechanism to facilitate patient engagement and patient-centered care, yet the use of these tools remains limited in the hospital setting. Although research in this area is growing, it is unclear how the use of acute care patient portals might affect outcomes, such as patient activation. OBJECTIVE: The aim of this study was to describe the use of an acute care patient portal and investigate its association with patient and care partner activation in the hospital setting. METHODS: We implemented an acute care patient portal on 6 acute care units over an 18-month period. We investigated the characteristics of the users (patients and their care partners) of the patient portal, as well as their use of the portal. This included the number of visits to each page, the number of days used, the length of the user's access period, and the average percent of days used during the access period. Patient and care partner activation was assessed using the short form of the patient activation measure (PAM-13) and the caregiver patient activation measure (CG-PAM). Comparisons of the activation scores were performed using propensity weighting and robust weighted linear regression. RESULTS: Of the 2974 randomly sampled patients, 59.01% (1755/2974) agreed to use the acute care patient portal. Acute care patient portal enrollees were younger, less sick, less likely to have Medicare as their insurer, and more likely to use the Partners Healthcare enterprise ambulatory patient portal (Patient Gateway). The most used features of the acute care patient portal were the laboratory test results, care team information, and medication list. Most users accessed the portal between 1 to 4 days during their hospitalization, and the average number of days used (logged in at least once per day) was 1.8 days. On average, users accessed the portal 42.69% of the hospital days during which it was available. There was significant association with patient activation on the neurology service (P<.001) and medicine service (P=.01), after the introduction of HIT tools and the acute care patient portal, but not on the oncology service. CONCLUSIONS: Portal users most often accessed the portal to view their clinical information, though portal usage was limited to only the first few days of enrollment. We found an association between the use of the portal and HIT tools with improved levels of patient activation. These tools may help facilitate patient engagement and improve outcomes when fully utilized by patients and care partners. Future study should leverage usage metrics to describe portal use and assess the impact of HIT tools on specific outcome measures in the hospital setting.

Assessing the Effectiveness of Engaging Patients and Their Families in the Three-Step Fall Prevention Process Across Modalities of an Evidence-Based Fall Prevention Toolkit: An Implementation Science Study.

BACKGROUND: Patient falls are a major problem in hospitals. The development of a Patient-Centered Fall Prevention Toolkit, Fall TIPS (Tailoring Interventions for Patient Safety), reduced falls by 25% in acute care hospitals by leveraging health information technology to complete the 3-step fall prevention process-(1) conduct fall risk assessments; (2) develop tailored fall prevention plans with the evidence-based interventions; and (3) consistently implement the plan. We learned that Fall TIPS was most effective when patients and family were engaged in all 3 steps of the fall prevention process. Over the past decade, our team developed 3 Fall TIPS modalities-the original electronic health record (EHR) version, a laminated paper version that uses color to provide clinical decision support linking patient-specific risk factors to the interventions, and a bedside display version that automatically populates the bedside monitor with the patients' fall prevention plan based on the clinical documentation in the EHR. However, the relative effectiveness of each Fall TIPS modality for engaging patients and family in the 3-step fall prevention process remains unknown. OBJECTIVE: This study aims to examine if the Fall TIPS modality impacts patient engagement in the 3-step fall prevention process and thus Fall TIPS efficacy. METHODS: To assess patient engagement in the 3-step fall prevention process, we conducted random audits with the question, "Does the patient/family member know their fall prevention plan?" In addition, audits were conducted to measure adherence, defined by the presence of the Fall TIPS poster at the bedside. Champions from 3 hospitals reported data from April to June 2017 on 6 neurology and 7 medical units. Peer-to-peer feedback to reiterate the best practice for patient engagement was central to data collection. RESULTS: Overall, 1209 audits were submitted for the patient engagement measure and 1401 for the presence of the Fall TIPS poster at the bedside. All units reached 80% adherence for both measures. While some units maintained high levels of patient engagement and adherence with the poster protocol, others showed improvement over time, reaching clinically significant adherence (>80%) by the final month of data collection. CONCLUSIONS: Each Fall TIPS modality effectively facilitates patient engagement in the 3-step fall prevention process, suggesting all 3 can be used to integrate evidence-based fall prevention practices into the clinical workflow. The 3 Fall TIPS modalities may prove an effective strategy for the spread, allowing diverse institutions to choose the modality that fits with the organizational culture and health information technology infrastructure.

Individualized sleep promotion in acute care hospitals: Identifying factors that affect patient sleep.

BACKGROUND/AIM: One major challenge of inpatient sleep promotion is that there is no "one-size-fits-all" intervention as patients' sleep may be bothered by different factors. A tool evaluating factors that disturb patient sleep is greatly needed as a foundation for generating a personalized action plan to address the patient's specific need for sleep. Unfortunately such tools are currently unavailable in clinical practice. In this study we developed and psychometrically evaluated a brief assessment tool for sleep disruptors important for hospitalized patients, the Factors Affecting Inpatient Sleep (FAIS) scale. METHODS: The FAIS items were developed by literature review and validated by content validity testing. A survey collected from 105 hospitalized patients was used to select the most significant sleep disruptors. Psychometric evaluation using survey data included item analysis, principal components analysis, and internal consistency reliability. RESULTS: The final FAIS scale included 14 items in three subscales explaining 56.4% of the total variance: 1) emotional or physical impairment due to illness or hospitalization; 2) sleep disturbance due to discomfort or care plan schedule; 3) sleep interruption due to hospital environment or medical care. The Cronbach's alpha coefficient for the FAIS scale was 0.87, and the reliability of the subscales ranged from 0.72 to 0.81. CONCLUSION: The FAIS is a brief tool assessing sleep disruptors important for patients, and is empirically grounded, judged to have content validity, and has demonstrated psychometric adequacy. The FAIS scale can be used to guide the development of an individualized patient-centered sleep promotion plan.

Prospective Evaluation of a Multifaceted Intervention to Improve Outcomes in Intensive Care: The Promoting Respect and Ongoing Safety Through Patient Engagement Communication and Technology Study.

OBJECTIVES: Studies comprehensively assessing interventions to improve team communication and to engage patients and care partners in ICUs are lacking. This study examines the effectiveness of a patient-centered care and engagement program in the medical ICU. DESIGN: Prospective intervention study. SETTING: Medical ICUs at large tertiary care center. PATIENTS: Two thousand one hundred five patient admissions (1,030 before and 1,075 during the intervention) from July 2013 to May 2014 and July 2014 to May 2015. INTERVENTIONS: Structured patient-centered care and engagement training program and web-based technology including ICU safety checklist, tools to develop shared care plan, and messaging platform. Patient and care partner access to online portal to view health information, participate in the care plan, and communicate with providers. MEASUREMENTS AND MAIN RESULTS: Primary outcome was aggregate adverse event rate. Secondary outcomes included patient and care partner satisfaction, care plan concordance, and resource utilization. We included 2,105 patient admissions, (1,030 baseline and 1,075 during intervention periods). The aggregate rate of adverse events fell 29%, from 59.0 per 1,000 patient days (95% CI, 51.8-67.2) to 41.9 per 1,000 patient days (95% CI, 36.3-48.3; p < 0.001), during the intervention period. Satisfaction improved markedly from an overall hospital rating of 71.8 (95% CI, 61.1-82.6) to 93.3 (95% CI, 88.2-98.4; p < 0.001) for patients and from 84.3 (95% CI, 81.3-87.3) to 90.0 (95% CI, 88.1-91.9; p < 0.001) for care partners. No change in care plan concordance or resource utilization. CONCLUSIONS: Implementation of a structured team communication and patient engagement program in the ICU was associated with a reduction in adverse events and improved patient and care partner satisfaction.

User-Centered Collaborative Design and Development of an Inpatient Safety Dashboard.

Patient safety remains a key concern in hospital care. This article summarizes the iterative participatory development, features, functions, and preliminary evaluation of a patient safety dashboard for interdisciplinary rounding teams on inpatient medical services. This electronic health record (EHR)-embedded dashboard collects real-time data covering 13 safety domains through web services and applies logic to generate stratified alerts with an interactive check-box function. The technological infrastructure is adaptable to other EHR environments. Surveyed users perceived the tool as highly usable and useful. Integration of the dashboard into clinical care is intended to promote communication about patient safety and facilitate identification and management of safety concerns.

Pilot Testing Fall TIPS (Tailoring Interventions for Patient Safety): a Patient-Centered Fall Prevention Toolkit.

BACKGROUND: Patient falls during an acute hospitalization cause injury, reduced mobility, and increased costs. The laminated paper Fall TIPS Toolkit (Fall TIPS) provides clinical decision support at the bedside by linking each patient's fall risk assessment with evidence-based interventions. Strategies were needed to integrate this evidence into clinical practice. METHODS: The Institute for Healthcare Improvement's Framework for Spread is the conceptual model for pilot implementation of Fall TIPS at Brigham and Women's Hospital (BWH; Boston) and Montefiore Medical Center (MMC; Bronx, New York). The key to translating the evidence into practice was engaging stakeholders by leveraging existing shared governance structures, identifying unit champions, holding training sessions for all staff, and implementing auditing to assess and provide feedback on protocol adherence and patient outcomes. RESULTS: BWH unit compliance with using Fall TIPS averaged 82%, the mean fall rate decreased from 3.28 to 2.80 falls per 1,000 patient-days from January through June 2015 versus 2016, and the mean fall with injury rate for these periods decreased from 1.00 to 0.54 per 1,000 patient-days. At MMC, compliance averaged 91%, but the mean fall rate increased marginally from 3.04 to 3.10, while the mean fall with injury rate decreased from 0.47 to 0.31 per 1,000 patient-days. Patient knowledge survey results show improvement in knowledge of the risks for falls and the ways to prevent falls. CONCLUSION: Engaging hospital and clinical leadership is critical in translating evidence-based care into clinical practice. Barriers to adoption of the protocol have been addressed and detailed to provide guidance for spread to other institutions.

Nurse, Patient, and Care Partner Perceptions of a Personalized Safety Plan Screensaver.

A patient safety plan dashboard was developed that captures disparate data from the electronic health record that is then displayed as a personalized bedside screensaver. The dashboard aligns all care team members, including patients and families, in the safety plan. The screensaver content includes icons that pertain to common geriatric syndromes. In two phases, interviews were conducted with nurses, nursing assistants, patients, and informal caregivers in a large, tertiary care center. End user perceptions of the content and interface of the personalized safety plan screensavers were identified and strategies to overcome the barriers to use for future iterations were defined. Many themes were identified, ranging from appreciation of the clinical decision support provided by the screensavers to the value of the safety-centric content. Differences emerged stemming from each group of end users' role on the care team. All feedback will inform requirements for improvements to the personalized safety plan screensaver. [Journal of Gerontological Nursing, 43(4), 15-22.].

Care coordination gaps due to lack of interoperability in the United States: a qualitative study and literature review.

BACKGROUND: Health information technology (HIT) could improve care coordination by providing clinicians remote access to information, improving legibility, and allowing asynchronous communication, among other mechanisms. We sought to determine, from a clinician perspective, how care is coordinated and to what extent HIT is involved when transitioning patients between emergency departments, acute care hospitals, skilled nursing facilities, and home health agencies in settings across the United States. METHODS: We performed a qualitative study with clinicians and information technology professionals from six regions of the U.S. which were chosen as national leaders in HIT. We analyzed data through a two person consensus approach, assigning responses to each of nine care coordination activities. We also conducted a literature review of MEDLINE®, CINAHL®, and Embase, analyzing results of studies that examined interventions to improve information transfer during transitions of care. RESULTS: We enrolled 29 respondents from 17 organizations and conducted six focus groups. Respondents reported how HIT is currently used for care coordination activities. HIT is currently used to monitor patients and to align systems-level resources with population needs. However, we identified multiple areas where the lack of interoperability leads to inefficient processes and missing data. Additionally, the literature review identified ten intervention studies that address information transfer, seven of which employed HIT and three of which utilized other communication methods such as telephone calls, faxed records, and nurse case management. CONCLUSIONS: Significant care coordination gaps exist due to the lack of interoperability across the United States. We must design, evaluate, and incentivize the use of HIT for care coordination. We should focus on the domains where we found the largest gaps: information transfer, systems to monitor patients, tools to support patients' self-management goals, and tools to link patients and their caregivers with community resources.

Clinical Decision Support and Perioperative Peripheral Nerve Injury: A Quality Improvement Project.

Decision support at the point of care has been demonstrated to be an effective tool in providing a safe environment and improving patient outcomes. The operating room is typically an area where advanced technology is introduced to nurses on a regular basis. This quality improvement project focused on preventing a peripheral nerve injury, which is an example of a postoperative adverse event that is considered preventable. Injury of a peripheral nerve is the result of compression, hyperextension, flexion, or ischemia surrounding the nerve. The goals for this project were to improve the knowledge of peripheral nerve injury of the operating room nurses, design and implement a peripheral nerve injury assessment screen that could provide decision support within the operating room record, improve the nursing documentation of peripheral nerve injury interventions, and (long term) decrease the incidence of peripheral nerve injury. A decision support screen within the operating room record was designed to supplement the operating room nurse's risk assessment for peripheral nerve injury. The components of this project involved a preliminary and postproject surveys on peripheral nerve injury knowledge, an educational presentation, and a retrospective random review of nursing documentation in the operating room electronic health records. Project results demonstrated a significant increase in nursing documentation of peripheral nerve injury interventions (63%-92%) and a positive attitude toward their exposure to basic decision support (P = .046). Recommendations for future studies and establishing a standardized coding system for peripheral nerve injury identification were identified.

Understanding physicians' behavior toward alerts about nephrotoxic medications in outpatients: a cross-sectional analysis.

BACKGROUND: Although most outpatients are relatively healthy, many have chronic renal insufficiency, and high override rates for suggestions on renal dosing have been observed. To better understand the override of renal dosing alerts in an outpatient setting, we conducted a study to evaluate which patients were more frequently prescribed contraindicated medications, to assess providers' responses to suggestions, and to examine the drugs involved and the reasons for overrides. METHODS: We obtained data on renal alert overrides and the coded reasons for overrides cited by providers at the time of prescription from outpatient clinics and ambulatory hospital-based practices at a large academic health care center over a period of 3 years, from January 2009 to December 2011. For detailed chart review, a group of 6 trained clinicians developed the appropriateness criteria with excellent inter-rater reliability (κ=0.93). We stratified providers by override frequency and then drew samples from the high- and low-frequency groups. We measured the rate of total overrides, rate of appropriate overrides, medications overridden, and the reason(s) for override. RESULTS: A total of 4120 renal alerts were triggered by 584 prescribers in the study period, among which 78.2% (3,221) were overridden. Almost half of the alerts were triggered by 40 providers and one-third was triggered by high-frequency overriders. The appropriateness rates were fairly similar, at 28.4% and 31.6% for high- and low-frequency overriders, respectively. Metformin, glyburide, hydrochlorothiazide, and nitrofurantoin were the most common drugs overridden. Physicians' appropriateness rates were higher than the rates for nurse practitioners (32.9% vs. 22.1%). Physicians with low frequency override rates had higher levels of appropriateness for metformin than the high frequency overriders (P=0.005). CONCLUSION: A small number of providers accounted for a large fraction of overrides, as was the case with a small number of drugs. These data suggest that a focused intervention targeting primarily these providers and medications has the potential to improve medication safety.