Digital Decision Support System Research Articles

Design of Digital and Intelligent Financial Decision Support System Based on Artificial Intelligence.

The quality of financial decision-making is very important to the future development of an enterprise, but it is often affected by the completeness of useful information for decision-making and the subjective factors of decision makers, and is often unstable. With the development of computer technology, the financial decision support system came into being, which improved the quality of financial decision to some extent. However, although the existing financial decision support system has achieved dataization to a certain extent, it still faces problems such as artificial leadership, insufficient intelligence, and poor decision-making efficiency, and cannot fully meet the needs of decision-makers. The explosion of artificial intelligence technology in recent years has provided potential improvements to financial decision support systems. In this article, we conduct a detailed analysis of the deficiencies in the current financial decision support system, build the mechanism and implementation path of the financial decision support system under artificial intelligence, and design a digital and intelligent financial decision support system. At the same time, we apply the proposed financial decision support system to the financial practice of X enterprise. Through the questionnaire survey, it is found that through the comprehensive application of artificial intelligence technology, the new system has a higher degree of intelligence than the existing system, and its construction can effectively improve the timeliness and accuracy of financial decision-making, while reducing the cost of financial decision-making. It is conducive to promoting the integration of management accounting and financial accounting.

A Wushu Referee’s Decision Support System Using Error Recognition Theory

To realize the recognition and scoring of Wushu Athletes’ wrong actions by electronic referees, reduce the difficulty of referees’ recognition, help reduce man-made errors and influence in the process of competition judgment, and make the referee work fairer and more scientific, therefore, this paper aims to make an in-depth study on the Wushu referee decision support system based on the theory and method of error recognition. Firstly, this paper discusses the overall architecture of the Wushu decision support system, analyzes the functions and database design of each system in the system, constructs a digital and information-based Wushu referee decision support system, and uses the error matrix logic equation to solve the recognition state and goal. The Nrej3d technology is introduced for collecting the characteristics of martial arts wrong actions, and matching recognition is completed by using the sports space, to complete the research on the decision support system of martial arts referees based on the theory and method of wrong recognition. The simulation results show that the method proposed in this paper is superior to other methods in the recognition accuracy and effective measurement of martial arts wrong movements and has a certain application value.

Tools supporting polypharmacy management in Italy: Factors determining digital technologies' intention to use in clinical practice.

Background and AimsINTERCheckWEB is one of the most outstanding digital technologies, that could be implemented at the hospital level, supporting the clinicians in the evaluation of the therapy appropriateness, reducing the potentially inappropriate prescriptions, for the improvement of the clinical decision‐making process. The paper aims at investigating the relationship between clinicians' behaviors towards digital decision support system in therapy appropriateness for elderly patients in polytherapy in medical departments, defining the factors that could influence clinicians to use INTERCheckWEB, for supporting drugs' prescription.MethodsA questionnaire was administered to 70 clinicians referring to Internal Medicine wards, of four Italian hospitals. The authors assessed how perceived usefulness, perceived ease of use, image, and output quality, would affect INTERCheckWeb intention to use. Inferential statistics, by means of a regression analysis, were conducted to define the main aspects useful to understand the factors impacting on such digital technology adoption in clinical practice.ResultsThe regression analysis reported that image, perceived ease of use and perceived usefulness, as well as the moderator effect of the voluntary use between the perceived usefulness and the intention to use, are the factors that most influence the use of INTERCheckWEB (adjusted R 2 = 0.870).ConclusionsResults demonstrated that clinicians would use INTERCheckWEB, when available, to identify all the information on situations that could be dangerous for the patients, thus limiting the drug–drug interactions, optimizing the overall patient's clinical pathway. Furthermore, the implementation of INTERCheckWEB could also contribute to the proper management of COVID‐19 patients, since both hospitalized and symptomatic COVID‐19 patients are frequently older, with comorbidities.

Assessment of the need for the use of digital medical decision support system in the diagnosis of diseases of the oral mucosa

Aim. To substantiate the need for the use of digital systems to support medical decision-making in the diagnosis of diseases of the oral mucosa.Materials and Methods. The study was conducted by the method of anonymous questionnaires, dedicated to the evaluation of the use of digital systems to support medical decision-making in the diagnosis of diseases of the oral mucosa, on the basis of the Institute of Dentistry I.M. Sechenov First Moscow State Medical University (Sechenov University). The study analyzed 200 questionnaires of doctors aged 20 to 70 years. The main methods used in the study: statistical method using the Kruskal-Wallis test, followed by pairwise comparison using the Mann-Whitney-Wilcoxon test (with Benjamin-Hochberg correction for multiple comparisons), questionnaire.Results. It was revealed that dentists with a long experience of medical practice from 15 to 20 years (77,8%) more often detect manifestations of diseases of the oral mucosa at a medical appointment. When analyzing the diagnosis of oral mucosa diseases, depending on the specialization of the dentist, it was found that this group of pathologies is more often determined by orthodontists(68,8%). At the same time, for the majority of specialists (59%), the diagnosis of diseases of the oral mucosa is difficult. Dentists of various specializations in most cases noted difficulties in determining a presumptive diagnosis (59.5%). At the same time, most specialists associated this with a lack of knowledge and experience in working with patients with diseases of the oral mucosa (66.5%) and with a large variety forms of diseases of the oral mucosa (54%) and resorted to the use of additional sources of information from textbooks, manuals, information articles, as well as the help of colleagues (64.5%). However, at clinical appointments, many doctors faced a lack of time to use reference literature (48.5%). As a result of the survey, it was also revealed that the majority of respondents (64,5%) would like to use the medical decision support system integrated into the medical information system of a medical institution.

Prospective evaluation of a clinical decision support system in psychological therapy.

Thus far, most applications in precision mental health have not been evaluated prospectively. This article presents the results of a prospective randomized-controlled trial investigating the effects of a digital decision support and feedback system, which includes two components of patient-specific recommendations: (a) a clinical strategy recommendation and (b) adaptive recommendations for patients at risk for treatment failure. Therapist-patient dyads (N = 538) in a cognitive behavioral therapy outpatient clinic were randomized to either having access to a decision support system (intervention group; n = 335) or not (treatment as usual; n = 203). First, treatment strategy recommendations (problem-solving, motivation-oriented, or a mix of both strategies) for the first 10 sessions were evaluated. Second, the effect of psychometric feedback enhanced with clinical problem-solving tools on treatment outcome was investigated. The prospective evaluation showed a differential effect size of about 0.3 when therapists followed the recommended treatment strategy in the first 10 sessions. Moreover, the linear mixed models revealed therapist symptom awareness and therapist attitude and confidence as significant predictors of an outcome as well as therapist-rated usefulness of feedback as a significant moderator of the feedback-outcome and the not on track-outcome associations. However, no main effects were found for feedback. The results demonstrate the importance of prospective studies and the high-quality implementation of digital decision support tools in clinical practice. Therapists seem to be able to learn from such systems and incorporate them into their clinical practice to enhance patient outcomes, but only when implementation is successful. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A retrospective evaluation of drug-drug interactions in patients admitted to Internal Medicine Departments in Palestinian Hospitals.

To measure the prevalence and identify risk factors associated with drug-drug interactions among patients admitted to internal medicine departments in Palestinian hospitals. A retrospective cross-sectional observational study was conducted. Data were obtained from patient files from the internal medicine departments in Palestinian hospitals from 1 September 2017, to 31 March 2018. The data collected included patient gender, age, length of hospitalization, medications prescribed, and the number of medications. The digital clinical decision support system IBM Micromedex® was used to assess potential drug-drug interactions. The number of patients included in this study is 513. The total number of potential drug-drug interactions detected in study participants is 1558. The average number of potential drug-drug interactions per patient was found to be 3 ± 3.9. Among study participants, 66.1% (n = 339) were found to have potential drug-drug interactions in their current medications. The most commonly encountered drug-drug interactions type was "major" drug-drug interaction, which was encountered in 43.6% (n = 681) of total detected drug-drug interactions. Other types of drug-drug interactions were encountered in 42% (n = 647), 14% (n = 224), and 0.4% (n = 6) which were moderate, minor, and contraindicated drug-drug interactions, respectively. Patients' age, number of medications, and length of hospitalization were associated with the increased risk of potential drug-drug interactions. The results indicated a high prevalence of potential drug-drug interactions in Palestinian hospitals, associated with polypharmacy, increased age, and increased length of hospitalization. Therefore, managing patient medication by a drug expert such as a clinical pharmacist to identify and resolve potential drug-drug interactions will possibly decrease the high prevalence of drug-drug interactions, prevent patient harm, and decrease the cost of hospitalization.

A Digital Twin Decision Support System for the Urban Facility Management Process.

The ever increasing pace of IoT deployment is opening the door to concrete implementations of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and environments. This digital replica could serve as the basis of a decision support system, providing insights into possible optimizations of resources in a smart city scenario. In this article, we discuss an extension of a prior work, presenting a detailed proof-of-concept implementation of a Digital Twin solution for the Urban Facility Management (UFM) process. The Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO) is a distributed geographical system, fed with and ingesting heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and build synthetic indexes measuring the activity level inside an area of interest. IPPODAMO takes into account potential interference from other stakeholders in the urban environment, enabling the informed scheduling of operations, aimed at minimizing interference and the costs of operations.

A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis.

BackgroundThere is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows.ObjectiveThe aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health.MethodsWe analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes.ResultsWe determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians’ reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis.ConclusionsThe application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

\u201cI must, and I can live with that\u201d: a thematic analysis of patients\u2019 perspectives on polypharmacy and a digital decision support system for GPs

BackgroundTo investigate patients’ perspectives on polypharmacy and the use of a digital decision support system to assist general practitioners (GPs) in performing medication reviews.MethodsQualitative interviews with patients or informal caregivers recruited from participants in a cluster-randomized controlled clinical trial (cRCT). The interviews were transcribed verbatim and analyzed using thematic analysis.ResultsWe conducted 13 interviews and identified the following seven themes: the patients successfully integrated medication use in their everyday lives, used medication plans, had both good and bad personal experiences with their drugs, regarded their healthcare providers as the main source of medication-related information, discussed medication changes with their GPs, had trusting relationships with them, and viewed the use of digital decision support tools for medication reviews positively. No unwanted adverse effects were reported.ConclusionsDespite drug-related problems, patients appeared to cope well with their medications. They also trusted their GPs, despite acknowledging polypharmacy to be a complex field for them. The use of a digital support system was appreciated and linked to the hope that reasons for selecting specific medication regimens would become more comprehensible. Further research with a more diverse sampling might add more patient perspectives.Trial registrationClinicalTrials.gov, NCT03430336. Registered on February 6, 2018.

Acute kidney injury and its progression in hospitalized patients-Results from a retrospective multicentre cohort study with a digital decision support system.

In this retrospective multicentric cohort study, we evaluate the potential benefits of a clinical decision support system (CDSS) for the automated detection of Acute kidney injury (AKI). A total of 80,389 cases, hospitalized from 2017 to 2019 at a tertiary care hospital (University of Leipzig Medical Center (ULMC)) and two primary care hospitals (Muldentalkliniken (MTL)) in Germany, were enrolled. AKI was defined and staged according to the Kidney disease: improving global outcomes (KDIGO) guidelines. Clinical and laboratory data was automatically collected from electronic patient records using the frameworks of the CDSS. In our cohort, we found an overall AKI incidence proportion of 12.1%. We identified 6,393/1,703/1,604 cases as AKI stage 1/2/3 (8.0%/2.1%/2.0%, respectively). Administrative coding with N17 (ICD-10-GM) was missing in 55.8% of all AKI cases with the potential for additional diagnosis related groups (DRG) reimbursement of 1,204,200 € in our study. AKI was associated with higher hospital mortality, increased length of hospitalisation and more frequent need of renal replacement therapy. A total of 19.1% of AKI cases (n = 1,848) showed progression to higher AKI stages (progressive AKI) during hospitalization. These cases presented with considerably longer hospitalization, higher rates of renal replacement therapy and increased mortality (p<0.001, respectively). Furthermore, progressive AKI was significantly associated with sepsis, shock, liver cirrhosis, myocardial infarction, and cardiac insufficiency. AKI, and especially its progression during hospitalization, is strongly associated with adverse outcomes. Our automated CDSS enables timely detection and bears potential to improve AKI outcomes, notably in cases of progressive AKI.

Call Characteristics of Patients Suspected of Transient Ischemic Attack (TIA) or Stroke During Out-of-Hours Service: A Comparison Between Men and Women.

Background: In the Netherlands, a digital decision support system for telephone triage at out-of-hours services in primary care (OHS-PC) is used. Differences in help-seeking behavior between men and women when transient ischemic attack (TIA) or stroke is suspected could potentially affect telephone triage and allocation of urgency.Aim: To assess patient and call characteristics and allocated urgencies between women and men who contacted OHS-PC with suspected TIA/stroke.Methods: A cross-sectional study of 1,266 telephone triage recordings of subjects with suspected neurological symptoms calling the OHS-PC between 2014 and 2016. The allocated urgencies were derived from the electronic medical records of the OHS-PC and the final diagnosis from the patient's own general practitioner, including diagnoses based on hospital specialist letters.Results: Five hundred forty-six men (mean age = 67.3 ± 17.1) and 720 women (mean age = 69.6 ± 19.5) were included. TIA/stroke was diagnosed in 294 men (54%) (mean age = 72.3 ± 13.6) and 366 women (51%) (mean age = 78.0 ± 13.8). In both genders, FAST (face-arm-speech test) symptoms were common in TIA/stroke (men 78%, women 82%) but also in no TIA/stroke (men 63%, women 62%). Men with TIA/stroke had shorter call durations than men without TIA/stroke (7.10 vs. 8.20 min, p = 0.001), whereas in women this difference was smaller and not significant (7.41 vs. 7.56 min, p = 0.41). Both genders were allocated high urgency in 75% of the final TIA/stroke cases.Conclusion: Overall, patient and call characteristics are mostly comparable between men and women, and these only modestly assist in identifying TIA/stroke. There were no gender differences in allocated urgencies after telephone triage in patients with TIA/stroke.

624-P: Sustained Effect in Diabetes Management Using a Digital Workflow and Decision Support System in Home Health Care

Introduction: Basal insulin is the first choice for geriatric persons with type 2 diabetes (T2D) who require insulin therapy. A digital workflow and decision support system (GlucoTab@MobileCare) with an integrated basal or basal-plus insulin algorithm has demonstrated to be an effective and safe treatment in geriatric persons with T2D receiving home health care during a three months study period. The aim of this study was to investigate the long-term effect on diabetes management following treatment by GlucoTab@MobileCare. Methods: Diabetes related aspects (glycemic control, acute hospital admissions, general practitioner (GP) consultations under routine conditions) were assessed in nine patients (5 female, age 77±10 years, BMI 27.7±4.7 kg/m2, HbA1c 60±13 mmol/mol [start of intervention] vs. 57±12 mmol/mol [end of intervention]) during the three months study intervention, six months before (n=9) and six months after (n=8, due to the death of one patient) study intervention. Data from electronic health records and diabetes documentation were analyzed. Results: Mean fasting blood glucose (BG) was 212±72 mg/dL before, 156±33 mg/dL during and 162±42 mg/dL after intervention. During the study, no acute hospital admissions due to BG derailments were observed. Eight derailments before and four derailments after intervention occurred. Causes for diabetes-related emergency department (ED) admissions were hyperglycemia (n=10), hypoglycemia (n=1) and undefined (n=1). Nine of the twelve admissions resulted in hospitalizations. GPs were consulted for diabetes management 19-times before and 16-times after GlucoTab@MobileCare treatment. Reasons for consultations were routine check-up (n=12), therapy adjustment (n=22) and HbA1c measurement (n=1). Conclusion: A sustained effect on glycemic control with reduced requirements for hospital admissions was observed six months after GlucoTab@MobileCare treatment, whereas GP consultations were not affected. Disclosure J. Kopanz: None. T. Pieber: Advisory Panel; Self; ADOCIA, Arecor, AstraZeneca, Eli Lilly and Company, Novo Nordisk A/S, Sanofi, Research Support; Self; AstraZeneca, Novo Nordisk A/S. J. K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Roche Diabetes Care, Speaker’s Bureau; Self; Abbott Diabetes, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Stock/Shareholder; Self; decide Clinical Software GmbH. L. K. Scholle: None. K. M. Lichtenegger: None. G. Ambrosch: None. A. Libiseller: None. F. Aberer: None. M. Pandis: None. K. Donsa: Stock/Shareholder; Self; decide Clinical Software GmbH. T. Truskaller: None.

Digital twin-driven decision support system for opportunistic preventive maintenance scheduling in manufacturing

Preventive maintenance interventions are scheduled in industrial systems to prevent machine failures and breakdowns, which are associated with the incurrence of repair, unavailability, and quality-related costs. The execution of such interventions, however, generally represents a penalty to a manufacturing system’s production throughput due to machine interruption requirements. By the use of a digital twin architecture, we develop a decision support system to schedule preventive maintenance interventions with the aim of minimizing production throughout penalties via the exploitation of real-time opportunities such as supply shortages, momentary machine idleness or machine breakdowns. The decision support system has its application demonstrated by a case in a furniture manufacturer in the State of Santa Catarina – Brazil.

Health Care Staff's Experiences of Engagement When Introducing a Digital Decision Support System for Wound Management: Qualitative Study.

BackgroundeHealth solutions such as digital decision support systems (DDSSs) have the potential to assist collaboration between health care staff to improve matters for specific patient groups. Patients with hard-to-heal ulcers have long healing times because of a lack of guidelines for structured diagnosis, treatment, and follow-up. Multidisciplinary collaboration in wound management teams is essential. A DDSS could offer a way of aiding improvement within wound management. The introduction of eHealth solutions into health care is complicated, and the engagement of the staff seems crucial. Factors influencing and affecting engagement need to be understood and considered for the introduction of a DDSS to succeed.ObjectiveThis study aims to describe health care staff’s experiences of engagement and barriers to and influencers of engagement when introducing a DDSS for wound management.MethodsThis study uses a qualitative approach. Interviews were conducted with 11 health care staff within primary (n=4), community (n=6), and specialist (n=1) care during the start-up of the introduction of a DDSS for wound management. The interviews focused on the staff’s experiences of engagement. Content analysis by Burnard was used in the data analysis process.ResultsA total of 4 categories emerged describing the participants’ experiences of engagement: a personal liaison, a professional commitment, an extended togetherness, and an awareness and understanding of the circumstances.ConclusionsThis study identifies barriers to and influencers of engagement, reinforcing that staff experience engagement through feeling a personal liaison and a professional commitment to make things better for their patients. In addition, engagement is nourished by sharing with coworkers and by active support and understanding from leadership.

Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis.

Drug-drug interactions (DDIs) and drug-gene interactions (DGIs) are well known mediators for adverse drug reactions (ADRs), which are among the leading causes of death in many countries. Because physiologically based pharmacokinetic (PBPK) modeling has demonstrated to be a valuable tool to improve pharmacotherapy affected by DDIs or DGIs, it might also be useful for precision dosing in extensive interaction network scenarios. The presented work proposes a novel approach to extend the prediction capabilities of PBPK modeling to complex drug-drug-gene interaction (DDGI) scenarios. Here, a whole-body PBPK network of simvastatin was established, including three polymorphisms (SLCO1B1 (rs4149056), ABCG2 (rs2231142), and CYP3A5 (rs776746)) and four perpetrator drugs (clarithromycin, gemfibrozil, itraconazole, and rifampicin). Exhaustive network simulations were performed and ranked to optimize 10,368 DDGI scenarios based on an exposure marker cost function. The derived dose recommendations were translated in a digital decision support system, which is available at simvastatin.precisiondosing.de. Although the network covers only a fraction of possible simvastatin DDGIs, it provides guidance on how PBPK modeling could be used to individualize pharmacotherapy in the future. Furthermore, the network model is easily extendable to cover additional DDGIs. Overall, the presented work is a first step toward a vision on comprehensive precision dosing based on PBPK models in daily clinical practice, where it could drastically reduce the risk of ADRs.

Assessing Drug-Drug Interaction Potential among Patients Admitted to Surgery Departments in Three Palestinian Hospitals.

Background Drug-drug interactions (DDIs) are a common issue that leads to adverse drug reactions in hospitals. Patients in the surgical department are expected to have potential DDIs that may lead to morbidity and mortality. Objectives To study potential DDI prevalence in the surgery departments in 3 hospitals in Palestine. Moreover, to identify pertinent factors that are associated with drug-drug interactions. Method A cross-sectional study in 3 governmental Palestinian hospitals: Palestine Medical Complex, Rafidia Hospital, and Beit Jala Hospital. Patients who are 20 years old or above and admitted to the surgical wards between September 2017 and February 2018 were included in the study. Patient demographics, all medications given in the hospital, and hospitalization period were obtained from medical files. The digital clinical decision support system Micromedex® was used for analysis and classification of possible drug interactions. Bivariate analysis and logistic regression were used to study the risk factors for developing DDIs. Results 502 patients were included in this report. The prevalence of potential DDIs among patients admitted to surgery wards in three Palestinian hospitals was 56%. The number of detected potential DDIs per patient was 2.22 ± 3.76. The number of prescribed medications (P < 0.001) was found to increase the possibility of having drug interactions. Conclusions DDIs in Palestinian hospitals are a prevalent problem, and caution should be taken when ordering medications to hospitalized patients in surgery departments.

Safe and Sufficient Glycemic Control by Using a Digital Clinical Decision Support System for Patients With Type 2 Diabetes in a Routine Setting on General Hospital Wards.

The aim was to investigate the applicability of a clinical decision support system in a real-world inpatient setting for patients with type 2 diabetes on general hospital wards.A total of 150 patients with type 2 diabetes requiring subcutaneous insulin therapy were treated with basal-bolus insulin therapy guided by a decision support system (GlucoTab) providing automated workflow tasks and suggestions for insulin dosing to health care professionals.By using the system, a mean daily blood glucose (BG) of 159 ± 32 mg/dL was achieved. 68.8% of measurements were in the target range (70 to <180 mg/dL). The percentage of BG values <40, <70, and ≥300 mg/dL was 0.02%, 2.2%, and 2.3%, respectively. Health care professionals' adherence to suggested insulin doses and workflow tasks was high (>93% and 91%, respectively).The decision support system facilitates safe and efficacious inpatient diabetes care by standardizing treatment workflow and providing decision support for basal-bolus insulin dosing.

A digital decision support system (selfBACK) for improved self-management of low back pain: a pilot study with 6-week follow-up

BackgroundVery few of the publicly available apps directed towards self-management of low back pain (LBP) have been rigorously tested and their theoretical underpinnings seldom described. The selfBACK app was developed in collaboration with end-users and clinicians and its content is supported by best evidence on self-management of LBP. The objectives of this pilot study were to investigate the basis for recruitment and screening procedures for the subsequent randomized controlled trial (RCT), to test the inclusion process in relation to questionnaires and app installation, and finally to investigate the change in primary outcome over time.MethodsThis single-armed pilot study enrolled 51 participants who had sought help for LBP of any duration from primary care (physiotherapy, chiropractic, or general practice) within the past 8 weeks. Participants were screened for eligibility using the PROMIS-Physical-Function-4a questionnaire. Participants were asked to use the selfBACK app for 6 weeks. The app provided weekly tailored self-management plans targeting physical activity, strength and flexibility exercises, and education. The construction of the self-management plans was achieved using case-based reasoning (CBR) methodology to capture and reuse information from previous successful cases. Participants completed the primary outcome pain-related disability (Roland-Morris Disability Questionnaire [RMDQ]) at baseline and 6-week follow-up along with a range of secondary outcomes. Metrics of app use were collected throughout the intervention period.ResultsFollow-up data at 6 weeks was obtained for 43 participants. The recruitment procedures were feasible, and the number needed to screen was acceptable (i.e., 1.6:1). The screening questionnaire was altered during the pilot study. The inclusion process, answering questionnaires and app installation, were feasible. The primary outcome (RMDQ) improved from 8.6 (SD 5.1) at baseline to 5.9 (SD 4.0) at 6-week follow-up (change score 1.8, 95% CI 0.7 to 2.9). Participants spent on average 134 min (range 0–889 min) using the app during the 6-week period.ConclusionThe recruitment, screening, and inclusion procedures were feasible for the subsequent RCT with a small adjustment. The improvement on the RMDQ from baseline to follow-up was small. Time pattern of app usage varied considerably between the participants.Trial registrationNCT03697759. Registered on August 10, 2018. https://clinicaltrials.gov/ct2/show/NCT03697759

Effects of using the MyFood decision support system on hospitalized patients' nutritional status and treatment: A randomized controlled trial.

Compliance to guidelines for disease-related malnutrition is documented as poor. The practice of using paper-based dietary recording forms with manual calculation of the patient's nutritional intake is considered cumbersome, time-consuming and unfeasible among the nurses and does often not lead to appropriate nutritional treatment. We developed the digital decision support system MyFood to deliver a solution to these challenges. MyFood is comprised of an app for patients and a website for nurses and includes functions for dietary recording, evaluation of intake compared to requirements, and a report to nurses including tailored recommendations for nutritional treatment and a nutritional care plan for documentation. The study aimed to investigate the effects of using the MyFood decision support system during hospital stay on adult patients' nutritional status, treatment and hospital length of stay. The main outcome measure was weight change. The study was a parallel-arm randomized controlled trial. Patients who were allocated to the intervention group used the MyFood app during their hospital stay and the nurses were encouraged to use the MyFood system. Patients who were allocated to the control group received routine care. We randomly assigned 100 patients (51.9±14y) to the intervention group (n=49) and the control group (n=51) between August 2018 and February 2019. Losses to follow-up were n=5 in the intervention group and n=1 in the control group. No difference was found between the two groups with regard to weight change. Malnutrition risk at discharge was present in 77% of the patients in the intervention group and 94% in the control group (p=0.019). Nutritional treatment was documented for 81% of the patients in the intervention group and 57% in the control group (p=0.011). A nutritional care plan was created for 70% of the intervention patients compared to 16% of the control patients (p<0.001). The intervention had no effect on weight change during hospital stay. A higher proportion of the patients in the control group was malnourished or at risk of malnutrition at hospital discharge compared to the patients in the intervention group. The documentation of nutritional intake, treatment and nutritional care plans was higher for the patients using the MyFood system compared to the control group. This trial was registered at clinicaltrials.gov (NCT03412695).

Brief, Web-Based Interventions to Motivate Smokers With Schizophrenia: Randomized Trial.

BackgroundIn-person motivational interventions increase engagement with evidence-based cessation treatments among smokers with schizophrenia, but access to such interventions can be limited because of workforce shortages and competing demands in mental health clinics. The use of digital technology to deliver interventions can increase access, but cognitive impairments in schizophrenia may impede the use of standard digital interventions. We developed an interactive, multimedia, digital motivational decision support system for smokers with schizophrenia (Let’s Talk About Smoking). We also digitalized a standard educational pamphlet from the National Cancer Institute (NCI Education). Both were tailored to reduce cognitive load during use.ObjectiveWe conducted a randomized trial of Let’s Talk About Smoking versus NCI Education to test whether the interactive motivational intervention was more effective and more appealing than the static educational intervention for increasing use of smoking cessation treatment, quit attempts, and abstinence among smokers with schizophrenia, accounting for the level of cognitive functioning.MethodsAdult smokers with schizophrenia (n=162) were enrolled in the study from 2014 to 2015, randomly assigned to an intervention condition, and assessed in person at 3- and 6-month follow-ups. Interventions were delivered on a laptop computer in a single session. All participants had access to standard, community-delivered cessation treatments during follow-up. Multivariate models were used to evaluate outcomes.ResultsTreatment initiation outcomes were not different between intervention conditions (27/84 [32%] for Let’s Talk About Smoking vs 36/78 [46%] for NCI Education; odds ratio [OR] 0.71 [95% CI 0.37-1.33]); 38.9% (63/162) of participants initiated treatment. Older age (OR 1.03 [95% CI 1.00-1.07]; P=.05), higher education (OR 1.21 [95% CI 1.04-1.41]; P=.03), and fewer positive symptoms (OR 0.87 [95% CI 0.80-0.96]; P=.01) predicted cessation treatment initiation, whereas level of cognition did not. The mean satisfaction and usability index score was higher for Let’s Talk About Smoking versus NCI Education (8.9 [SD 1.3] vs 8.3 [SD 2.1]; t 120.7=2.0; P=.045). Quit attempts (25/84, 30% vs 36/78, 46%; estimate [Est]=−0.093, SE 0.48; P=.85) and abstinence (1/84, 1% vs 6/78, 7%; χ21=3.4; P=.07) were not significantly different between intervention conditions. Cognitive functioning at baseline (Est=1.47, SE 0.47; P=.002) and use of any behavioral or medication cessation treatment (Est=1.43, SE 0.47; P=.003) predicted quit attempts with self-reported abstinence over the 6-month follow-up.ConclusionsThe interactive, multimedia intervention was not more effective than the static, text-based intervention among smokers with schizophrenia. Both tailored digital interventions resulted in levels of treatment engagement and quit attempts that were similar to findings from previous studies of in-person interventions, confirming the potential role of digital interventions to educate and motivate smokers with schizophrenia to use cessation treatment and to quit smoking. These findings indicate that additional cessation treatment is needed after brief education or motivational interventions, and that cessation treatment should be adjusted for people with cognitive impairment.Trial RegistrationClinicalTrials.gov NCT02086162; https://clinicaltrials.gov/show/NCT02086162