Optimizing delivery to meet demand for integrative medicine services in an academic hospital setting: A pilot study

Vitek, Sairey; Erickson, Emma E.; Cutshall, Susanne M.; Slack, Stephanie M.; Rodgers, N.; Smidt, Jessica M.; Jordan, Mary; Bauer, Brent A.; Chon, Tony Y.

doi:10.1016/j.explore.2020.03.001

Cited by 8 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…26 Finally, the referral for IM and IM delivery were entirely based on the clinical needs of the patients. Factors influencing the optimal use and implementation of IM therapies 27 for pain relief in hospitals should be the focus of future research. The research on the implementation of acupuncture in the inpatient setting 28,29 is at an early stage.…”

Section: Discussionmentioning

confidence: 99%

Significant Pain Reduction in Hospitalized Patients Receiving Integrative Medicine Interventions by Clinical Population and Accounting for Pain Medication

Dusek

Rivard

Griffin

et al. 2021

The Journal of Alternative and Complementary Medicine

View full text Add to dashboard Cite

Background: Prior research has reported that integrative medicine (IM) therapies reduce pain in inpatients, but without controlling for important variables. Here, the authors extend prior research by assessing pain reduction while accounting for each patient's pain medication status and clinical population.Methods: The initial data set consisted of 7,106 inpatient admissions, aged ‡18 years, between July 16, 2012, and December 15, 2014. Patients' electronic health records were used to obtain data on demographic, clinical measures, and pain medication status during IM.Results: The final data set included first IM therapies delivered during 3,635 admissions. Unadjusted average pre-IM pain was 5.33 (95% confidence interval [CI]: 5.26 to 5.41) and post-IM pain was 3.31 (95% CI: 3.23 to 3.40) on a 0-10 scale. Pain change adjusted for severity of illness, clinical population, sex, treatment, and pain medication status during IM was significant and clinically meaningful with an average reduction of -1.97 points (95% CI: -2.06 to -1.86) following IM. Adjusted average pain was reduced in all clinical populations, with largest and smallest pain reductions in maternity care (-2.34 points [95% CI: -2.56 to -2.14]) and orthopedic (-1.71 points [95% CI: -1.98 to -1.44]) populations. Pain medication status did not have a statistically significant association on pain change. Decreases were observed regardless of whether patients were taking narcotic medications and/or nonsteroidal anti-inflammatory drugs versus no pain medications.Conclusions: For the first time, inpatients receiving IM reported significant and clinically meaningful pain reductions during a first IM session while accounting for pain medications and across clinical populations. Future implementation research should be conducted to optimize identification/referral/delivery of IM therapies within hospitals.Clinical Trials.gov #NCT02190240.

show abstract

Section: Discussionmentioning

confidence: 99%

Significant Pain Reduction in Hospitalized Patients Receiving Integrative Medicine Interventions by Clinical Population and Accounting for Pain Medication

Dusek

Rivard

Griffin

et al. 2021

The Journal of Alternative and Complementary Medicine

View full text Add to dashboard Cite

show abstract

“…Medication management is also a high-risk nursing access. In the process of medication, wrong medication, less medication, and missing medication will affect the whole nursing process [23,24].…”

Section: Nurses' Cognition Formentioning

confidence: 99%

Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization

Liu

2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

It was to explore the application of nursing defect management evaluation and deep learning in nursing process reengineering optimization. This study first selects the root cause analysis method to analyse the nursing defect management, then realizes the classification of data features according to the convolution neural network (CNN) in deep learning (DL) and uses the constructed training set and verification set to obtain the required plates and feature extraction. Based on statistical analysis and data mining, this study makes statistical analysis of nursing data from a macroperspective, improves Apriori algorithm through simulation, and analyses nursing data mining from a microperspective. The constructed deep learning model is used, CNN network training is conducted on the selected SVHN dataset, the required data types are classified, the data are analysed by using the improved Apriori algorithm, and nurses’ knowledge of nursing process rules is investigated and analysed. The cognition of nursing staff on process optimization and their participation in training were analyzed, the defects in the nursing process were summarized, and the nursing process reengineering was analyzed. The results show that compared with Apriori algorithm, the running time difference of the improved Apriori algorithm is relatively small. With the increase of data recording times, the line trend of the improved algorithm gradually eases, the advantages gradually appear, and the efficiency of data processing is more obvious. The results showed that after the optimization of nursing process, the effect of long-term specialized nursing was significantly higher than that of long-term nursing. Health education was improved by 7.57%, clinical nursing was improved by 6.55%, ward management was improved by 9.85%, and service humanization was improved by 8.97%. In summary, the reoptimization of nursing process is conducive to reduce the defects in nursing. In the data analysis and rule generation based on deep learning network, the reoptimization of nursing process can provide reference for decision-making departments to improve long-term nursing, improve the quality and work efficiency of clinical nurses, and is worthy of clinical promotion.

show abstract

“…Among the health services that can be provided to patients are therapeutic processes such as surgical operations. The increasing demand for these processes in today's world has made them one of the key services provided by medical centers 3 . Moreover, due to the conditions of patients in the operating room and due to the relevant treatment actions, surgeries are usually considered as high‐risk processes.…”

Section: Introductionmentioning

confidence: 99%

“…The increasing demand for these processes in today's world has made them one of the key services provided by medical centers. 3 Moreover, due to the conditions of patients in the operating room and due to the relevant treatment actions, surgeries are usually considered as highrisk processes. However, despite adequate attention to standardization and documentation of procedures required for accreditation of therapeutic processes, subjects such as modeling, monitoring, and increasing the accuracy and precision of these processes have not been adequately addressed.…”

Section: Introductionmentioning

confidence: 99%

Monitoring multistage multivariate therapeutic processes using risk‐adjusted model‐based group multivariate EWMA control chart

Rasouli

Noorossana

Samimi

2022

Quality & Reliability Eng

View full text Add to dashboard Cite

There exist multitude of therapeutic processes and the results are commonly observed during various dependent steps. For studying such processes that are referred to as multistage therapeutic processes, two concepts are of particular importance; risk adjustment and cascade property. To monitor such processes, a variety of control charts including model‐based control charts are used. In order to design model‐based control charts, analysts must first recognize a suitable model to identify the multistage processes by considering process risks and cascade property. Based on the identified model, the control charts can be proposed. In this study, a risk‐adjusted time‐variant linear state space model is introduced. Afterward, the model order and its parameters are estimated based on Hankel singular value decomposition (HSVD) and prediction error minimization (PEM) methods. Then, the group multivariate exponentially weighted moving average (GMEWMA) control chart is used to monitor a multistage multivariate therapeutic process. To evaluate the performance of the model‐based control chart, a simulation study as well as a two‐stage thyroid cancer surgery was used. Results show that the proposed control chart performs well for predicting and monitoring of multistage multivariate therapeutic processes in real world.

show abstract

Optimizing delivery to meet demand for integrative medicine services in an academic hospital setting: A pilot study

Cited by 8 publications

References 10 publications

Significant Pain Reduction in Hospitalized Patients Receiving Integrative Medicine Interventions by Clinical Population and Accounting for Pain Medication

Significant Pain Reduction in Hospitalized Patients Receiving Integrative Medicine Interventions by Clinical Population and Accounting for Pain Medication

Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization

Monitoring multistage multivariate therapeutic processes using risk‐adjusted model‐based group multivariate EWMA control chart

Contact Info

Product

Resources

About