Barriers and Facilitators to Integrating Acupuncture into the U.S. Health Care System: A Scoping Review.

Background: Acupuncture is a widely practiced complementary and integrative health modality that has multiple clinical applications. The use of acupuncture in the United States is rapidly increasing. Although studies have shown the efficacy and effectiveness of acupuncture for various ailments, the integration of acupuncture into the U.S. health care system remains a challenge. Little is known about the factors affecting this integration. Objective: To provide a systematic review of the barriers and facilitators affecting the integration of acupuncture into the U.S. health care system. Methods: Four electronic databases were searched. Three independent reviewers were involved in the screening and data charting processes. Findings were synthesized and categorized into four levels based on the Social Ecological Model. Results: A total of 22 studies were included in the final review. The barriers and facilitators affecting the integration of acupuncture were mapped into four levels (individual, interpersonal, organizational, and policy). The most frequently reported barriers and facilitators were mapped into the Social Ecological Model constructs within the "Individual" level (i.e., beliefs and attitudes of acupuncture, and practical issues) and the "Organizational" level (i.e., credentialing, space and facility, referral system). Conclusion: This review has identified and synthesized the breadth of evidence on the barriers and facilitators to the integration of acupuncture into the U.S. health care system. Results of this review will guide future implementation studies to develop and test implementation strategies to integrate acupuncture into the U.S. health care system.

Representation of child and youth participation within the Unified Medical Language System (UMLS).

PURPOSE: To examine (1) how much participation is represented in the benchmark Unified Medical Language System (UMLS) resource, and (2) to what extent that representation reflects the definition of child and youth participation and/or its related constructs per the family of Participation-Related Constructs framework. MATERIALS AND METHODS: We searched and analysed UMLS concepts related to the term "participation." Identified UMLS concepts were rated according to their representation of participation (i.e., attendance, involvement, both) as well as participation-related constructs using deductive content analysis. RESULTS: 363 UMLS concepts were identified. Of those, 68 had at least one English definition, resulting in 81 definitions that were further analysed. Results revealed 2 definitions (2/81; 3%; 2/68 UMLS concepts) representing participation "attendance" and 18 definitions (18/81; 22%; 14/68 UMLS concepts) representing participation "involvement." No UMLS concept definition represented both attendance and involvement (i.e., participation). Most of the definitions (11/20; 55%; 9/16 UMLS concepts) representing attendance or involvement also represent a participation-related construct. CONCLUSION(S): The representation of participation within the UMLS is limited and poorly aligned with the contemporary definition of child and youth participation. Expanding ontological resources to represent child and youth participation is needed to enable better data analytics that reflect contemporary paediatric rehabilitation practice.

The representation of participation within the Unified Medical Language System (UMLS) is limited and poorly aligned with the contemporary definition of child and youth participation.From a contemporary paediatric rehabilitation perspective, using the current UMLS concepts for data analytics might result in misrepresentation of child and youth participation.There is need to expand ontological resources within the UMLS to fully and exclusively represent participation dimensions (attendance and involvement) in daily life activities to enable better data analytics that reflect contemporary paediatric rehabilitation practice.

Interprofessional Documentation: Where is Everyone?

EHRs have become a means for interprofessional practice in healthcare. Following a rapid review, a lack of study on interprofessional documentation (IPD) was identified, especially in professions other than physicians and nurses. We proposed the definition of IPD as two or more providers documenting in an electronic system to coordinate care. Our review identified this topic needs future studies.

Collecting patient-reported outcome measures in the electronic health record: Lessons from the NIH pragmatic trials Collaboratory.

The NIH Pragmatic Trials Collaboratory supports the design and conduct of 27 embedded pragmatic clinical trials, and many of the studies collect patient reported outcome measures as primary or secondary outcomes. Study teams have encountered challenges in the collection of these measures, including challenges related to competing health care system priorities, clinician's buy-in for adoption of patient-reported outcome measures, low adoption and reach of technology in low resource settings, and lack of consensus and standardization of patient-reported outcome measure selection and administration in the electronic health record. In this article, we share case examples and lessons learned, and suggest that, when using patient-reported outcome measures for embedded pragmatic clinical trials, investigators must make important decisions about whether to use data collected from the participating health system's electronic health record, integrate externally collected patient-reported outcome data into the electronic health record, or collect these data in separate systems for their studies.

Natural Language Processing to Classify Caregiver Strategies Supporting Participation Among Children and Youth with Craniofacial Microsomia and Other Childhood-Onset Disabilities.

Customizing participation-focused pediatric rehabilitation interventions is an important but also complex and potentially resource intensive process, which may benefit from automated and simplified steps. This research aimed at applying natural language processing to develop and identify a best performing predictive model that classifies caregiver strategies into participation-related constructs, while filtering out non-strategies. We created a dataset including 1,576 caregiver strategies obtained from 236 families of children and youth (11-17 years) with craniofacial microsomia or other childhood-onset disabilities. These strategies were annotated to four participation-related constructs and a non-strategy class. We experimented with manually created features (i.e., speech and dependency tags, predefined likely sets of words, dense lexicon features (i.e., Unified Medical Language System (UMLS) concepts)) and three classical methods (i.e., logistic regression, naïve Bayes, support vector machines (SVM)). We tested a series of binary and multinomial classification tasks applying 10-fold cross-validation on the training set (80%) to test the best performing model on the held-out test set (20%). SVM using term frequency-inverse document frequency (TF-IDF) was the best performing model for all four classification tasks, with accuracy ranging from 78.10 to 94.92% and a macro-averaged F1-score ranging from 0.58 to 0.83. Manually created features only increased model performance when filtering out non-strategies. Results suggest pipelined classification tasks (i.e., filtering out non-strategies; classification into intrinsic and extrinsic strategies; classification into participation-related constructs) for implementation into participation-focused pediatric rehabilitation interventions like Participation and Environment Measure Plus (PEM+) among caregivers who complete the Participation and Environment Measure for Children and Youth (PEM-CY). Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-023-00149-y.

Monitoring and responding to signals of suicidal ideation in pragmatic clinical trials: Lessons from the GRACE trial for Chronic Sickle Cell Disease Pain.

Sickle cell disease (SCD) is a hemoglobin disorder and the most common genetic disorder that affects 100,000 Americans and millions worldwide. Adults living with SCD have pain so severe that it often requires opioids to keep it in control. Depression is a major global public health concern associated with an increased risk in chronic medical disorders, including in adults living with sickle cell disease (SCD). A strong relationship exists between suicidal ideation, suicide attempts, and depression. Researchers enrolling adults living with SCD in pragmatic clinical trials are obligated to design their methods to deliberately monitor and respond to symptoms related to depression and suicidal ideation. This will offer increased protection for their participants and help clinical investigators meet their fiduciary duties. This article presents a review of this sociotechnical milieu that highlights, analyzes, and offers recommendations to address ethical considerations in the development of protocols, procedures, and monitoring activities related to suicidality in depressed patients in a pragmatic clinical trial.

Use of a Person-Centered Narrative Intervention in an Outpatient Palliative Care Setting: A Feasibility Study.

Person-centered narrative interventions offer potential solutions to facilitate a connection between the person receiving care and the person delivering the care, to improve quality of care, and positively impact a patient's biopsychosocial well-being. This single-arm feasibility study investigates patient-reported outcomes and barriers/facilitators to the implementation of an all-virtually delivered person-centered narrative intervention into the person's electronic health record. Overall, electronic data collection for the patient-reported outcomes was feasible. All 15 participants felt participating in the study was "easy" and "enjoyable," and "not a burden." The facilitators of implementation included: "helpful to the clinician," "appreciated looking at me as whole person," "be seen and heard," "had a connection and trust," and "felt comfortable and relaxing." The barriers to implementation included: "completing all the paperwork," "being rushed for time to complete the PCNI," and some "emotion" during collection of narrative. The use of person-centered narrative interventions is a way to deploy dedicated tools to shift dehumanized healthcare delivery to a more humanized person-centered care that treats people as experts in their own life narratives by incorporating their beliefs, values, and preferences into their plan of care.

Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory.

Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges-incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology-that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.

Equity and bias in electronic health records data.

Embedded pragmatic clinical trials (ePCTs) are conducted during routine clinical care and have the potential to increase knowledge about the effectiveness of interventions under real world conditions. However, many pragmatic trials rely on data from the electronic health record (EHR) data, which are subject to bias from incomplete data, poor data quality, lack of representation from people who are medically underserved, and implicit bias in EHR design. This commentary examines how the use of EHR data might exacerbate bias and potentially increase health inequities. We offer recommendations for how to increase generalizability of ePCT results and begin to mitigate bias to promote health equity.

An Electronically Delivered Person-Centered Narrative Intervention for Persons Receiving Palliative Care: Protocol for a Mixed Methods Study.

BACKGROUND: In the health care setting, electronic health records (EHRs) are one of the primary modes of communication about patients, but most of this information is clinician centered. There is a need to consider the patient as a person and integrate their perspectives into their health record. Incorporating a patient's narrative into the EHR provides an opportunity to communicate patients' cultural values and beliefs to the health care team and has the potential to improve patient-clinician communication. This paper describes the protocol to evaluate the integration of an adapted person-centered narrative intervention (PCNI). This adaptation builds on our previous research centered on the implementation of PCNIs. The adaptation for this study includes an all-electronic delivery of a PCNI in an outpatient clinical setting. OBJECTIVE: This research protocol aims to evaluate the feasibility, usability, and effects of the all-electronic delivery of a PCNI in an outpatient setting on patient-reported outcomes. The first objective of this study is to identify the barriers and facilitators of an internet-based-delivered PCNI from the perspectives of persons living with serious illness and their clinicians. The second objective is to conduct acceptability, usability, and intervention fidelity testing to determine the essential requirements for the EHR integration of an internet-based-delivered PCNI. The third objective is to test the feasibility of the PCNI in an outpatient clinic setting. METHODS: Using a mixed method design, this single-arm intervention feasibility study was delivered over approximately 3 to 4 weeks. Patient participant recruitment was conducted via screening outpatient palliative care clinic schedules weekly for upcoming new palliative care patient visits and then emailing potential patient participants to notify them about the study. The PCNI was delivered via email and Zoom app. Patient-reported outcome measures were completed by patient participants at baseline, 24 to 48 hours after PCNI, and after the initial palliative care clinic visit, approximately 1 month after baseline. Inclusion criteria included having the capacity to give consent and having an upcoming initial outpatient palliative care clinic visit. RESULTS: The recruitment of participants began in April 2021. A total of 189 potential patient participants were approached via email, and 20 patient participants were enrolled, with data having been collected from May 2021 to September 2022. A total of 7 clinician participants were enrolled, with a total of 3 clinician exit interviews and 1 focus group (n=5), which was conducted in October 2022. Data analysis is expected to be completed by the end of June 2023. CONCLUSIONS: The findings from this study, combined with those from other PCNI studies conducted in acute care settings, have the potential to influence clinical practices and policies and provide innovative avenues to integrate more person-centered care delivery. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41787.

Hybrid effectiveness-implementation trial of guided relaxation and acupuncture for chronic sickle cell disease pain (GRACE): A protocol.

Background: People with sickle cell disease frequently use complementary and integrative therapies to cope with their pain, yet few studies have evaluated their effectiveness. The 3-arm, 3-site pragmatic Hybrid Effectiveness-implementation Trial of Guided Relaxation and Acupuncture for Chronic Sickle Cell Disease Pain (GRACE) has 3 priorities: (1) evaluate guided relaxation and acupuncture to improve pain control; (2) determine the most appropriate and effective treatment sequence for any given patient based on their unique characteristics; and (3) describe the processes and structures required to implement guided relaxation and acupuncture within health care systems. Methods: Participants (N = 366) are being recruited and randomized 1:1:1 to one of 2 intervention groups or usual care. The acupuncture intervention group receives 10 sessions over approximately 5 weeks. The guided relaxation intervention group receives access to video sessions ranging from 2 to 20 min each viewed daily over 5 weeks. The usual care group receives the standard of clinical care for sickle cell disease. Participants are re-randomized at 6 weeks depending on their pain impact score. Assessments occur at 6 weeks, 12 weeks, and 24 weeks. The primary outcome is the change in pain impact score and secondary measures include opioid use, anxiety, depression, sleep, pain catastrophizing, substance use, global impression of change, constipation, and hospitalizations. The GRACE study uses the Consolidated Framework for Implementation Research to plan, execute, and evaluate the associated implementation processes. Conclusion: The results from GRACE will represent a critical step toward improving management of pain affecting patients with sickle cell disease.ClinicalTrials.gov Identifier: NCT04906447.

Relations of Current and Past Cancer with Severe Outcomes among 104,590 Hospitalized COVID-19 Patients: The COVID EHR Cohort at the University of Wisconsin.

BACKGROUND: There is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics. METHODS: Electronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses. Moderation by patient characteristics, vaccination status, cancer type, and year of the pandemic was examined. RESULTS: 6.8% of the patients had current (n = 7,141) and 6.5% had past (n = 6,749) cancer diagnoses. Current cancer predicted both severe outcomes but past cancer did not; adjusted odds ratios (aOR) for mortality were 1.58 [95% confidence interval (CI), 1.46-1.70] and 1.04 (95% CI, 0.96-1.13), respectively. Mortality rates decreased over the pandemic but the incremental risk of current cancer persisted, with the increment being larger among younger vs. older patients. Prior COVID-19 vaccination reduced mortality generally and among those with current cancer (aOR, 0.69; 95% CI, 0.53-0.90). CONCLUSIONS: Current cancer, especially among younger patients, posed a substantially increased risk for death and ICU admission among patients with COVID-19; prior COVID-19 vaccination mitigated the risk associated with current cancer. Past history of cancer was not associated with higher risks for severe COVID-19 outcomes for most cancer types. IMPACT: This study clarifies the characteristics that modify the risk associated with cancer on severe COVID-19 outcomes across the first 20 months of the COVID-19 pandemic. See related commentary by Egan et al., p. 3.

Smoking Status, Nicotine Medication, Vaccination, and COVID-19 Hospital Outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) Study.

INTRODUCTION: Available evidence is mixed concerning associations between smoking status and COVID-19 clinical outcomes. Effects of nicotine replacement therapy (NRT) and vaccination status on COVID-19 outcomes in smokers are unknown. METHODS: Electronic health record data from 104 590 COVID-19 patients hospitalized February 1, 2020 to September 30, 2021 in 21 U.S. health systems were analyzed to assess associations of smoking status, in-hospital NRT prescription, and vaccination status with in-hospital death and ICU admission. RESULTS: Current (n = 7764) and never smokers (n = 57 454) did not differ on outcomes after adjustment for age, sex, race, ethnicity, insurance, body mass index, and comorbidities. Former (vs never) smokers (n = 33 101) had higher adjusted odds of death (aOR, 1.11; 95% CI, 1.06-1.17) and ICU admission (aOR, 1.07; 95% CI, 1.04-1.11). Among current smokers, NRT prescription was associated with reduced mortality (aOR, 0.64; 95% CI, 0.50-0.82). Vaccination effects were significantly moderated by smoking status; vaccination was more strongly associated with reduced mortality among current (aOR, 0.29; 95% CI, 0.16-0.66) and former smokers (aOR, 0.47; 95% CI, 0.39-0.57) than for never smokers (aOR, 0.67; 95% CI, 0.57, 0.79). Vaccination was associated with reduced ICU admission more strongly among former (aOR, 0.74; 95% CI, 0.66-0.83) than never smokers (aOR, 0.87; 95% CI, 0.79-0.97). CONCLUSIONS: Former but not current smokers hospitalized with COVID-19 are at higher risk for severe outcomes. SARS-CoV-2 vaccination is associated with better hospital outcomes in COVID-19 patients, especially current and former smokers. NRT during COVID-19 hospitalization may reduce mortality for current smokers. IMPLICATIONS: Prior findings regarding associations between smoking and severe COVID-19 disease outcomes have been inconsistent. This large cohort study suggests potential beneficial effects of nicotine replacement therapy on COVID-19 outcomes in current smokers and outsized benefits of SARS-CoV-2 vaccination in current and former smokers. Such findings may influence clinical practice and prevention efforts and motivate additional research that explores mechanisms for these effects.

The first 20 months of the COVID-19 pandemic: Mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems.

MAIN OBJECTIVE: There is limited information on how patient outcomes have changed during the COVID-19 pandemic. This study characterizes changes in mortality, intubation, and ICU admission rates during the first 20 months of the pandemic. STUDY DESIGN AND METHODS: University of Wisconsin researchers collected and harmonized electronic health record data from 1.1 million COVID-19 patients across 21 United States health systems from February 2020 through September 2021. The analysis comprised data from 104,590 adult hospitalized COVID-19 patients. Inclusion criteria for the analysis were: (1) age 18 years or older; (2) COVID-19 ICD-10 diagnosis during hospitalization and/or a positive COVID-19 PCR test in a 14-day window (+/- 7 days of hospital admission); and (3) health system contact prior to COVID-19 hospitalization. Outcomes assessed were: (1) mortality (primary), (2) endotracheal intubation, and (3) ICU admission. RESULTS AND SIGNIFICANCE: The 104,590 hospitalized participants had a mean age of 61.7 years and were 50.4% female, 24% Black, and 56.8% White. Overall risk-standardized mortality (adjusted for age, sex, race, ethnicity, body mass index, insurance status and medical comorbidities) declined from 16% of hospitalized COVID-19 patients (95% CI: 16% to 17%) early in the pandemic (February-April 2020) to 9% (CI: 9% to 10%) later (July-September 2021). Among subpopulations, males (vs. females), those on Medicare (vs. those on commercial insurance), the severely obese (vs. normal weight), and those aged 60 and older (vs. younger individuals) had especially high mortality rates both early and late in the pandemic. ICU admission and intubation rates also declined across these 20 months. CONCLUSIONS: Mortality, intubation, and ICU admission rates improved markedly over the first 20 months of the pandemic among adult hospitalized COVID-19 patients although gains varied by subpopulation. These data provide important information on the course of COVID-19 and identify hospitalized patient groups at heightened risk for negative outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).

Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review.

Background: There is increased interest in using artificial intelligence (AI) to provide participation-focused pediatric re/habilitation. Existing reviews on the use of AI in participation-focused pediatric re/habilitation focus on interventions and do not screen articles based on their definition of participation. AI-based assessments may help reduce provider burden and can support operationalization of the construct under investigation. To extend knowledge of the landscape on AI use in participation-focused pediatric re/habilitation, a scoping review on AI-based participation-focused assessments is needed. Objective: To understand how the construct of participation is captured and operationalized in pediatric re/habilitation using AI. Methods: We conducted a scoping review of literature published in Pubmed, PsycInfo, ERIC, CINAHL, IEEE Xplore, ACM Digital Library, ProQuest Dissertation and Theses, ACL Anthology, AAAI Digital Library, and Google Scholar. Documents were screened by 2-3 independent researchers following a systematic procedure and using the following inclusion criteria: (1) focuses on capturing participation using AI; (2) includes data on children and/or youth with a congenital or acquired disability; and (3) published in English. Data from included studies were extracted [e.g., demographics, type(s) of AI used], summarized, and sorted into categories of participation-related constructs. Results: Twenty one out of 3,406 documents were included. Included assessment approaches mainly captured participation through annotated observations (n = 20; 95%), were administered in person (n = 17; 81%), and applied machine learning (n = 20; 95%) and computer vision (n = 13; 62%). None integrated the child or youth perspective and only one included the caregiver perspective. All assessment approaches captured behavioral involvement, and none captured emotional or cognitive involvement or attendance. Additionally, 24% (n = 5) of the assessment approaches captured participation-related constructs like activity competencies and 57% (n = 12) captured aspects not included in contemporary frameworks of participation. Conclusions: Main gaps for future research include lack of: (1) research reporting on common demographic factors and including samples representing the population of children and youth with a congenital or acquired disability; (2) AI-based participation assessment approaches integrating the child or youth perspective; (3) remotely administered AI-based assessment approaches capturing both child or youth attendance and involvement; and (4) AI-based assessment approaches aligning with contemporary definitions of participation.

Disentangling the Role of Religiosity in Human Papillomavirus Vaccination Amidst COVID-19 Pandemic.

Religion is a complex and sociocultural driver of human papillomavirus (HPV) vaccination decisions, but its exact role has been mixed/unclear. We used a cross-sectional study of 342 Christian parents to examine the associations between the three domains of religiosity (organizational, non-organizational, and intrinsic) and the intention to (i) seek HPV information and (ii) receive the HPV vaccine. Organizational religiosity was the only domain that was positively associated with information-seeking intention regardless of the type of covariates included. Mixed findings in the association between religiosity and HPV vaccination decisions may depend on the religiosity domain being assessed.

Adherence to Analgesics Among Outpatients Seriously Ill With Cancer.

BACKGROUND: Adherence to prescribed analgesics for patients seriously ill with cancer pain is essential for comfort. OBJECTIVE: The objective of this study was to determine the analgesic adherence in seriously ill patients with cancer and its association with clinical and demographic characteristics. METHODS: This is a cross-sectional study. At home, 202 patients with cancer (mean age, 59.9 ± 14.2 years; 58% female, 48% Black, and 42% White) admitted to hospice/palliative care completed measures on a pen tablet: PAIN Report It, Symptom Distress Scale, mood state item, Pittsburgh Sleep Quality Index item, and Pain Management Index. RESULTS: The mean current pain intensity was 4.4 ± 2.9, and the mean worst pain in the past 24 hours was 7.2 ± 2.7. More than one-half of participants were not satisfied with their pain level (54%) and reported their pain was more intense than they wanted to tolerate for 18 hours or longer in the last 24 hours (51%). Only 12% were not prescribed analgesics appropriate for the intensity of their pain. Adherence rates were variable: nonsteroidal anti-inflammatory drugs (0.63 ± 0.50), adjuvants (0.93 ± 0.50), World Health Organization step 2 opioids (0.63 ± 0.49), and step 3 opioids (0.80 ± 0.40). With setting/clinical/demographic variables in the model, dose intervals of less than 8 hours were associated with less adherence ( P < .001). CONCLUSION: Little progress has been made toward improving analgesic adherence even in settings providing analgesics without cost. Research focused on targeting analgesic dose intervals and barriers not related to cost is needed. IMPLICATION FOR PRACTICE: Dose intervals of 8 hours or longer were significantly associated with higher adherence rates; therefore, use of longer-acting analgesics is one strategy to improve pain control at the end of life.

Masking Between Reserved Alarm Sounds of the IEC 60601-1-8 International Medical Alarm Standard: A Systematic, Formal Analysis.

OBJECTIVE: In this work, we systematically evaluated the reserved alarm sounds of the IEC 60601-1-8 international medical alarm standard to determine when and how they can be totally and partially masked. BACKGROUND: IEC 60601-1-8 gives engineers instruction for creating human-perceivable auditory medical alarms. This includes reserved alarm sounds: common types of alarms where each is a tonal melody. Even when this standard is honored, practitioners still fail to hear alarms, causing practitioner nonresponse and, thus, potential patient harm. Simultaneous masking, a condition where one or more alarms is imperceptible in the presence of other concurrently sounding alarms due to limitations of the human sensory system, is partially responsible for this. METHODS: In this research, we use automated proof techniques to determine if masking can occur in a modeled configuration of medical alarms. This allows us to determine when and how reserved alarm sound can mask other reserved alarms and to explore parameters to address discovered problems. RESULTS: We report the minimum number of other alarm sounds it takes to both totally and partially mask each of the high-, medium-, and low-priority alarm sounds from the standard. CONCLUSIONS: Significant masking problems were found for both the total and partial masking of high-, medium-, and low-priority reserved alarm sounds. APPLICATION: We show that discovered problems can be mitigated by setting alarm volumes to standard values based on priority level and by randomizing the timing of alarm tones.

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture.

Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hospital mortality. Assessing the likelihood of death is a challenging and time-consuming task due to a large number of influencing factors. Healthcare providers are interested in the detection of ICU patients at higher risk, such that risk factors can possibly be mitigated. While such severity scoring methods exist, they are commonly based on a snapshot of the health conditions of a patient during the ICU stay and do not specifically consider a patient's prior medical history. In this paper, a process mining/deep learning architecture is proposed to improve established severity scoring methods by incorporating the medical history of diabetes patients. First, health records of past hospital encounters are converted to event logs suitable for process mining. The event logs are then used to discover a process model that describes the past hospital encounters of patients. An adaptation of Decay Replay Mining is proposed to combine medical and demographic information with established severity scores to predict the in-hospital mortality of diabetes ICU patients. Significant performance improvements are demonstrated compared to established risk severity scoring methods and machine learning approaches using the Medical Information Mart for Intensive Care III dataset.