Intravenous Medication Administration Safety with Smart Infusion Pumps in the Neonatal Intensive Care Unit: An Observational Study.

INTRODUCTION: Infants in the neonatal intensive care unit (NICU) are among the most vulnerable patient populations and medication errors are a significant source of risk and harm to neonates. Smart infusion pumps have been implemented to support the safe medication administration process; however, the effect of using smart infusion pumps on medication safety in the NICU is still unclear. METHODS: We conducted an observational study with a prospective point-prevalence approach to investigate intravenous (IV) medication administration errors in the NICU at one academic medical center in the USA. Observations were conducted in 48 days in a 3-month data collection period in 2019. RESULTS: We observed a total of 441 patients with 905 IV medication administrations during the data collection period. The total number of errors was 130 (14.4 per 100 administrations). Of these, the most frequent errors were selecting the wrong drug library entry (5.3 per 100 administrations), unauthorized medication (0.7 per 100 administrations), and wrong dose (0.6 per 100 administrations). Sixty-eight errors (7.5 per 100 administrations) were unlikely to cause harm despite reaching the patient (category C errors), while the rest did not reach the patient. CONCLUSION: We identified the medication errors, which was unique to NICU populations, but no harm to the patients were identified. Most errors occurred due to a lack of compliance of using smart pump technology; therefore, potential exists to maximize safety related to medication administration practices in the NICU through hospital policy change and increasing adherence to appropriate use of smart pump technology.

Providers' and Patients' Perspectives on Diagnostic Errors in the Acute Care Setting.

BACKGROUND: Diagnostic errors (DEs) have been studied extensively in ambulatory care, but less work has been done in the acute care setting. In this study, the authors examined health care providers' and patients' perspectives about the classification of DEs, the main causes and scope of DEs in acute care, the main gaps in current systems, and the need for innovative solutions. METHODS: A qualitative mixed methods study was conducted, including semistructured interviews with health care providers and focus groups with patient advisors. Using grounded theory approach, thematic categories were derived from the interviews and focus groups. RESULTS: The research team conducted interviews with 17 providers and two focus groups with seven patient advisors. Both providers and patient advisors struggled to define and describe DEs in acute care settings. Although participants agreed that DEs pose a significant risk to patient safety, their perception of the frequency of DEs was mixed. Most participants identified communication failures, lack of comfort with diagnostic uncertainty, incorrect clinical evaluation, and cognitive load as key causes of DEs. Most respondents believed that non-information technology (IT) tools and processes (for example, communication improvement strategies) could significantly reduce DEs. CONCLUSION: The study findings represent an important supplement to our understanding of DEs in acute care settings and the advancement of a culture of patient safety in the context of patient-centered care and patient engagement. Health care organizations should consider the key factors identified in this study when trying to create a culture that engages clinicians and patients in reducing DEs.

Comparison of a Voluntary Safety Reporting System to a Global Trigger Tool for Identifying Adverse Events in an Oncology Population.

OBJECTIVE: There is a lack of research on adverse event (AE) detection in oncology patients, despite the propensity for iatrogenic harm. Two common methods include voluntary safety reporting (VSR) and chart review tools, such as the Institute for Healthcare Improvement's Global Trigger Tool (GTT). Our objective was to compare frequency and type of AEs detected by a modified GTT compared with VSR for identifying AEs in oncology patients in a larger clinical trial. METHODS: Patients across 6 oncology units (from July 1, 2013, through May 29, 2015) were randomly selected. Retrospective chart reviews were conducted by a team of nurses and physicians to identify AEs using the GTT. The VSR system was queried by the department of quality and safety of the hospital. Adverse event frequencies, type, and harm code for both methods were compared. RESULTS: The modified GTT detected 0.90 AEs per patient (79 AEs in 88 patients; 95% [0.71-1.12] AEs per patient) that were predominantly medication AEs (53/79); more than half of the AEs caused harm to the patients (41/79, 52%), but only one quarter were preventable (21/79; 27%). The VSR detected 0.24 AEs per patient (21 AEs in 88 patients; 95% [0.15-0.37] AEs per patient), a large plurality of which were medication/intravenous related (8/21); more than half did not cause harm (70%). Only 2% of the AEs (2/100) were detected by both methods. CONCLUSIONS: Neither the modified GTT nor the VSR system alone is sufficient for detecting AEs in oncology patient populations. Further studies exploring methods such as automated AE detection from electronic health records and leveraging patient-reported AEs are needed.

The Impact of Smart Pump Interoperability on Errors in Intravenous Infusion Administrations: A Multihospital Before and After Study.

OBJECTIVE: The objective of this study was to assess the frequency, type, and severity of errors associated with intravenous medication administration before and after smart pump interoperability. METHODS: We conducted an observational study at a community healthcare system before and after implementing smart pump interoperability. Point prevalence methodology was used to collect data on medication administration and errors in adult inpatient settings. RESULTS: Observations were completed for 350 infusions preintervention (178 patients) and 367 postintervention (200 patients). Total errors significantly decreased from 401 (114.6 per 100 infusions) to 354 (96.5 per 100 infusions, P = 0.02). Administration errors decreased from 144 (41.1 per 100 infusions) to 119 (32.4 per 100 infusions, P = 0.12). Expired medication errors significantly reduced from 11 (3.1 per 100 infusions) to 2 (0.5 per 100 infusions, P = 0.02). Errors involving high-risk medications significantly reduced from 45 (12.8 per 100 infusions) to 25 (6.8 per 100 infusions, P = 0.01). Errors involving continuous medications significantly reduced from 44 (12.6 per 100 infusions) to 22 (6.0 per 100 infusions, P = 0.005). When comparing programming type, manual programming resulted in 115 (77.2%) of administration and user documentation errors compared with 34 errors (22.8%) that occurred when autoprogramming was used. Of these, errors involving high-risk medications reduced from 21 (84.0%) to 4 (16.0%) after using autoprogramming. CONCLUSIONS: Smart pump interoperability resulted in a 16% reduction in medication administration errors. Despite using dose error reduction software and autoprogramming, some types of errors persisted. Further studies are needed to understand how technology use can be optimized.

Unique Patient-Reported Hospital Safety Concerns With Online Tool: MySafeCare.

BACKGROUND: Hospitalized patients and their care partners have valuable and unique perspectives of the medical care they receive. Direct and real-time reporting of patients' safety concerns, though limited in the acute care setting, could provide opportunities to improve patient care. METHODS: We implemented the MySafeCare (MSC) application on six acute care units for 18 months as part of a patient-centered health information technology intervention to promote engagement and safety in the acute care setting. The web-based application allowed hospitalized patients to submit safety concerns anonymously and in real time. We describe characteristics of patient submissions including their categorizations. We evaluated rates of submissions to MSC and compared them with rates of submissions to the Patient Family Relations department at the hospital. In addition, we performed thematic analysis of narrative concerns submitted to the application. RESULTS: We received 46 submissions to MSC and 33% of concerns received were anonymous. The overall rate of submissions was 0.6 submissions per 1000 patient-days and was considerably lower than the rate of submissions to the Patient Family Relations during the same period (4.1 per 1000 patient-days). Identified themes of narrative concerns included unmet care needs and preferences, inadequate communication, and concerns about safety of care. CONCLUSIONS: Although the submission rate to the application was low, MSC captured important content directly from hospitalized patients or their care partners. A web-based patient safety reporting tool for patients should be studied further to understand patient and care partner use and willingness to engage, as well as potential effects on patient safety outcomes.

Identifying nursing documentation patterns associated with patient deterioration and recovery from deterioration in critical and acute care settings.

OBJECTIVES: Nursing documentation behavior within electronic health records may reflect a nurse's concern about a patient and can be used to predict patient deterioration. Our study objectives were to quantify variations in nursing documentation patterns, confirm those patterns and variations with clinicians, and identify which patterns indicate patient deterioration and recovery from clinical deterioration events in the critical and acute care settings. METHODS: We collected patient data from electronic health records and conducted a regression analysis to identify different nursing documentation patterns associated with patient outcomes resulting from clinical deterioration events in the intensive care unit (ICU) and acute care unit (ACU). The primary outcome measures were whether patients were discharged alive from the hospital or expired during their hospital encounter. Secondary outcome measures were clinical deterioration events. RESULTS: In the ICU, the increased documentation of heart rate, body temperature, and withheld medication administrations were significantly associated with inpatient mortality. In the ACU, the documentation of blood pressure, respiratory rate with comments, singular vital signs, and withheld medications were significantly related to inpatient mortality. In contrast, the documentation of heart rate and "as needed" medication administrations were significantly associated with patient survival to discharge in the ACU. CONCLUSION: We successfully identified and confirmed the clinical relevancy of the nursing documentation patterns indicative of patient deterioration and recovery from clinical deterioration events in both the ICU and ACU.

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.

Evaluating the Impact of Radio Frequency Identification Retained Surgical Instruments Tracking on Patient Safety: Literature Review.

BACKGROUND: Retained surgical instruments (RSI) are one of the most serious preventable complications in operating room settings, potentially leading to profound adverse effects for patients, as well as costly legal and financial consequences for hospitals. Safety measures to eliminate RSIs have been widely adopted in the United States and abroad, but despite widespread efforts, medical errors with RSI have not been eliminated. OBJECTIVE: Through a systematic review of recent studies, we aimed to identify the impact of radio frequency identification (RFID) technology on reducing RSI errors and improving patient safety. METHODS: A literature search on the effects of RFID technology on RSI error reduction was conducted in PubMed and CINAHL (2000-2016). Relevant articles were selected and reviewed by 4 researchers. RESULTS: After the literature search, 385 articles were identified and the full texts of the 88 articles were assessed for eligibility. Of these, 5 articles were included to evaluate the benefits and drawbacks of using RFID for preventing RSI-related errors. The use of RFID resulted in rapid detection of RSI through body tissue with high accuracy rates, reducing risk of counting errors and improving workflow. CONCLUSIONS: Based on the existing literature, RFID technology seems to have the potential to substantially improve patient safety by reducing RSI errors, although the body of evidence is currently limited. Better designed research studies are needed to get a clear understanding of this domain and to find new opportunities to use this technology and improve patient safety.

Information Needs and the Use of Documentation to Support Collaborative Decision-Making: Implications for the Reduction of Central Line-Associated Blood Stream Infections.

It is clear that interdisciplinary communication and collaboration have the potential to mitigate healthcare-associated harm, yet there is limited research on how communication through documentation in the patient record can support collaborative decision making. Understanding what information is needed to support collaborative decision making is necessary to design electronic health information systems that facilitate effective communication and, ultimately, safe care. To explore this issue, we focused on information needs related to central venous catheter management and the prevention of central line-associated blood stream infections. Semistructured interviews were conducted with nurses working in an intensive care unit. Interview transcripts were analyzed using inductive thematic analysis. Three themes were identified: (1) challenges managing documentation in multiple places in the absence of formal documentation processes for central venous catheter management; (2) lack of standardized decision-making processes for managing central venous catheters; and (3) oral communication holds it together. Our findings provide a foundation for the development of EHR functional requirements that enhance communication regarding the management of central venous catheters and facilitate the prompt removal of unnecessary lines.

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.

Use, Perceived Usability, and Barriers to Implementation of a Patient Safety Dashboard Integrated within a Vendor EHR.

BACKGROUND: Preventable adverse events continue to be a threat to hospitalized patients. Clinical decision support in the form of dashboards may improve compliance with evidence-based safety practices. However, limited research describes providers' experiences with dashboards integrated into vendor electronic health record (EHR) systems. OBJECTIVE: This study was aimed to describe providers' use and perceived usability of the Patient Safety Dashboard and discuss barriers and facilitators to implementation. METHODS: The Patient Safety Dashboard was implemented in a cluster-randomized stepped wedge trial on 12 units in neurology, oncology, and general medicine services over an 18-month period. Use of the Dashboard was tracked during the implementation period and analyzed in-depth for two 1-week periods to gather a detailed representation of use. Providers' perceptions of tool usability were measured using the Health Information Technology Usability Evaluation Scale (rated 1-5). Research assistants conducted field observations throughout the duration of the study to describe use and provide insight into tool adoption. RESULTS: The Dashboard was used 70% of days the tool was available, with use varying by role, service, and time of day. On general medicine units, nurses logged in throughout the day, with many logins occurring during morning rounds, when not rounding with the care team. Prescribers logged in typically before and after morning rounds. On neurology units, physician assistants accounted for most logins, accessing the Dashboard during daily brief interdisciplinary rounding sessions. Use on oncology units was rare. Satisfaction with the tool was highest for perceived ease of use, with attendings giving the highest rating (4.23). The overall lowest rating was for quality of work life, with nurses rating the tool lowest (2.88). CONCLUSION: This mixed methods analysis provides insight into the use and usability of a dashboard tool integrated within a vendor EHR and can guide future improvements and more successful implementation of these types of tools.

Mining clinical phrases from nursing notes to discover risk factors of patient deterioration.

OBJECTIVE: Early identification and treatment of patient deterioration is crucial to improving clinical outcomes. To act, hospital rapid response (RR) teams often rely on nurses' clinical judgement typically documented narratively in the electronic health record (EHR). We developed a data-driven, unsupervised method to discover potential risk factors of RR events from nursing notes. METHODS: We applied multiple natural language processing methods, including language modelling, word embeddings, and two phrase mining methods (TextRank and NC-Value), to identify quality phrases that represent clinical entities from unannotated nursing notes. TextRank was used to determine the important word-sequences in each note. NC-Value was then used to globally rank the locally-important sequences across the whole corpus. We evaluated our method both on its accuracy compared to human judgement and on the ability of the mined phrases to predict a clinical outcome, RR event hazard. RESULTS: When applied to 61,740 hospital encounters with 1,067 RR events and 778,955 notes, our method achieved an average precision of 0.590 to 0.764 (when excluding numeric tokens). Time-dependent covariates Cox model using the phrases achieved a concordance index of 0.739. Clustering the phrases revealed clinical concepts significantly associated with RR event hazard. DISCUSSION: Our findings demonstrate that our minimal-annotation, unsurprised method can rapidly mine quality phrases from a large amount of nursing notes, and these identified phrases are useful for downstream tasks, such as clinical outcome predication and risk factor identification.

Lessons learned implementing a complex and innovative patient safety learning laboratory project in a large academic medical center.

OBJECTIVE: The objective of this paper is to share challenges, recommendations, and lessons learned regarding the development and implementation of a Patient Safety Learning Laboratory (PSLL) project, an innovative and complex intervention comprised of a suite of Health Information Technology (HIT) tools integrated with a newly implemented Electronic Health Record (EHR) vendor system in the acute care setting at a large academic center. MATERIALS AND METHODS: The PSLL Administrative Core engaged stakeholders and study personnel throughout all phases of the project: problem analysis, design, development, implementation, and evaluation. Implementation challenges and recommendations were derived from direct observations and the collective experience of PSLL study personnel. RESULTS: The PSLL intervention was implemented on 12 inpatient units during the 18-month study period, potentially impacting 12,628 patient admissions. Challenges to implementation included stakeholder engagement, project scope/complexity, technology/governance, and team structure. Recommendations to address each of these challenges were generated, some enacted during the trial, others as lessons learned for future iterative refinements of the intervention and its implementation. CONCLUSION: Designing, implementing, and evaluating a suite of tools integrated within a vendor EHR to improve patient safety has a variety of challenges. Keys to success include continuous stakeholder engagement, involvement of systems and human factors engineers within a multidisciplinary team, an iterative approach to user-centered design, and a willingness to think outside of current workflows and processes to change health system culture around adverse event prevention.

Identifying nurses' concern concepts about patient deterioration using a standard nursing terminology.

OBJECTIVES: Nurse concerns documented in nursing notes are important predictors of patient risk of deterioration. Using a standard nursing terminology and inputs from subject-matter experts (SMEs), we aimed to identify and define nurse concern concepts and terms about patient deterioration, which can be used to support subsequent automated tasks, such as natural language processing and risk predication. METHODS: Group consensus meetings with nurse SMEs were held to identify nursing concerns by grading Clinical Care Classification (CCC) system concepts based on clinical knowledge. Next, a fundamental lexicon was built placing selected CCC concepts into a framework of entities and seed terms to extend CCC granularity. RESULTS: A total of 29 CCC concepts were selected as reflecting nurse concerns. From these, 111 entities and 586 seed terms were generated into a fundamental lexicon. Nursing concern concepts differed across settings (intensive care units versus non-intensive care units) and unit types (medicine versus surgery units). CONCLUSIONS: The CCC concepts were useful for representing nursing concern as they encompass a nursing-centric conceptual framework and are practical in lexicon construction. It enabled the codification of nursing concerns for deteriorating patients at a standardized conceptual level. The boundary of selected CCC concepts and lexicons were determined by the SMEs. The fundamental lexicon offers more granular terms that can be identified and processed in an automated fashion.

Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event.

BACKGROUND: In the hospital setting, it is crucial to identify patients at risk for deterioration before it fully develops, so providers can respond rapidly to reverse the deterioration. Rapid response (RR) activation criteria include a subjective component ("worried about the patient") that is often documented in nurses' notes and is hard to capture and quantify, hindering active screening for deteriorating patients. OBJECTIVES: We used unsupervised machine learning to automatically discover RR event risk/protective factors from unstructured nursing notes. METHODS: In this retrospective cohort study, we obtained nursing notes of hospitalized, nonintensive care unit patients, documented from 2015 through 2018 from Partners HealthCare databases. We applied topic modeling to those notes to reveal topics (clusters of associated words) documented by nurses. Two nursing experts named each topic with a representative Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) concept. We used the concepts along with vital signs and demographics in a time-dependent covariates extended Cox model to identify risk/protective factors for RR event risk. RESULTS: From a total of 776,849 notes of 45,299 patients, we generated 95 stable topics, of which 80 were mapped to 72 distinct SNOMED CT concepts. Compared with a model containing only demographics and vital signs, the latent topics improved the model's predictive ability from a concordance index of 0.657 to 0.720. Thirty topics were found significantly associated with RR event risk at a 0.05 level, and 11 remained significant after Bonferroni correction of the significance level to 6.94E-04, including physical examination (hazard ratio [HR] = 1.07, 95% confidence interval [CI], 1.03-1.12), informing doctor (HR = 1.05, 95% CI, 1.03-1.08), and seizure precautions (HR = 1.08, 95% CI, 1.04-1.12). CONCLUSION: Unsupervised machine learning methods can automatically reveal interpretable and informative signals from free-text and may support early identification of patients at risk for RR events.

CONCERN Factorial Design Survey (FDS) Methods Test: Using REDCap as a Survey Platform.

We assessed the feasibility of using REDCap as a factorial design survey (FDS) platform. REDCap lacks randomization and automation functionality, requiring the development of a workaround. A template survey was created containing all vignettes, copied for each survey instance and edited to hide unwanted content. REDCap configuration required three hours for forty-two surveys. The utilized "copy-and-hide" workaround was successful, providing quasi-automation and reasonable labor-time. Additional strategies are planned using REDCap's Data Dictionary and other survey software.

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.

Intravenous Infusion Administration: A Comparative Study of Practices and Errors Between the United States and England and Their Implications for Patient Safety.

INTRODUCTION: Intravenous medication administration is widely reported to be error prone. Technologies such as smart pumps have been introduced with a view to reducing these errors. An international comparison could provide evidence of their effectiveness, including consideration of contextual factors such as regulatory systems and local cultures. OBJECTIVES: The aim of this study was to investigate similarities and differences in practices and error types involving intravenous medication administration in the United States and England, and summarise methodological differences necessary to perform these parallel studies. METHODS: We drew on findings of separate point prevalence studies conducted across hospitals in each country. In these, we compared what was being administered at the time of observation with the prescription and relevant policies, errors were classified by type and severity, and proportions of infusions featuring each error type were calculated. We also reviewed what adaptations to the US protocol were needed for England. Authors independently reviewed findings from both studies and proposed themes for comparison. In online meetings, each country's research team clarified assumptions and explained their findings. RESULTS: Key themes included commonalities and contrasts in methods, findings, practices and policies. Although US sites made greater use of smart infusion devices, and had more precisely defined requirements around infusion device use, patterns of errors were similar. Differences among clinical contexts within each country were as marked as differences across countries. Regulatory and quality control systems shape practices, but causal relationships are complex. CONCLUSION: Infusion administration is a complex adaptive system with multiple interacting agents (professionals, patients, software systems, etc.) that respond in rich ways to their environments; safety depends on complex, interrelated factors.

Fall Prevention Self-Management Among Older Adults: A Systematic Review.

CONTEXT: Adequate self-management could minimize the impact of falls in older adults. The efficacy of fall prevention self-management interventions has been widely studied, yet little is known about why some older adults engage in fall prevention self-management actions and behaviors, whereas others do not. Through a systematic review of fall prevention self-management studies, this study identified characteristics and the personal, social, and environmental factors of older adults who engage in self-management actions and behaviors. EVIDENCE ACQUISITION: Medical and nursing literature related to fall prevention self-management was searched in PubMed, Embase, and CINAHL (1997-2017), and relevant publications were selected by three researchers to assess whether the papers included subject characteristics and their fall prevention self-management actions and behaviors. GRADE (Grading of Recommendations, Assessment, Development and Evaluations) was used by the researchers to assess the quality of the included studies and to determine the significance of the extracted characteristics. EVIDENCE SYNTHESIS: Searching literature through 2017, a total of 972 papers were identified, and 28 papers remained after removing those that did not meet inclusion criteria. Nine papers that addressed subject characteristics in relation to the study outcomes were included in a sub-analysis. The authors identified the following characteristics of older adults who participated in fall prevention self-management actions and behaviors: younger males, not living alone and with self-reported good health, having greater fear of falling and high fall prevention self-efficacy, and possessing high motivation for engagement with self-management activities. CONCLUSIONS: The systematic literature review revealed the personal characteristics of older adults who engage in fall prevention self-management actions and behaviors.

Patients as Partners in the Hospital Setting: Engagement Methods in Research, Quality Improvement and Beyond.

Benefits have been demonstrated when patients and family members have been meaningfully engaged in all stages of research beginning with topic generation. When this engagement is done well, research becomes more relevant to those receiving health services, and there is an increased sense of accountability and transparency provided. However, health informatics researchers have not consistently used patient and family member engagement methods despite their many potential benefits. This panel will outline various methods that can be used to engage patients and family members in all stages of the research process in a health informatics context. In addition to these strategies, this panel will provide real-life examples of how patients and family members have been engaged in health informatics research in both Canada and the United States.