Combining Data Imputation and Statistics to Design a Clinical Decision Support System for Post-Operative Pain Monitoring

Pombo, Nuno; Rebelo, Paulo; Araújo, Pedro; Viana, José Antônio

doi:10.1016/j.procs.2015.08.621

Cited by 7 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Commensurate with previous reviews on this topic [ 6 , 7 ], a majority of articles analyzed reported improvement in practitioner performance [ 16 - 18 , 21 , 23 - 25 , 28 - 31 , 33 - 35 , 37 , 38 , 40 , 44 , 45 , 47 - 50 ], but contrary to the previous reviews, our review found articles that reported patient outcomes, and a majority were positive outcomes [ 15 , 17 , 20 , 21 , 23 , 26 , 27 , 31 , 32 , 34 , 35 , 37 , 39 , 41 - 48 , 50 ]. Although 9 articles did not discuss practitioner performance [ 15 , 20 , 26 , 27 , 32 , 39 , 41 , 43 ], only 5 articles reported no difference in productivity [ 19 , 22 , 36 , 42 , 46 ].…”

Section: Discussionsupporting

confidence: 74%

“…Overall , the research generally supports the efficiency of CDSS technologies for practitioner performance [ 16 - 18 , 21 , 23 - 25 , 28 - 31 , 33 - 35 , 37 , 38 , 40 , 44 , 45 , 47 - 50 ] and effectiveness in patient medical outcomes [ 15 , 17 , 20 , 21 , 23 , 26 , 27 , 31 , 32 , 34 , 35 , 37 , 39 , 41 - 48 , 50 ]; however, a further in-depth review of their effectiveness, in particular for aspects such as the avoidance of alert fatigue and extension of CDSS utility, is important. Decision-support tools extend beyond the practitioner to the patient, and some tools are not software-based but based on patient-reported data [ 46 ].…”

Section: Discussionmentioning

confidence: 62%

“…As illustrated, 66% (25/38) of the occurrences of themes identified 10 positive indicators of practitioner performance [ 16 - 18 , 21 , 23 - 25 , 28 - 31 , 33 - 35 , 37 , 38 , 40 , 44 , 45 , 47 - 50 ]. Practitioner performance was reported as more accurate prescribing, improved screening of patients, improved overall performance, increased awareness of patient conditions, improved follow-up due to better communication with patients, improved accuracy of diagnosis, improved documentation, improved benchmarking, improved care plans, and improved buy-in of CDSSs.…”

Section: Resultsmentioning

confidence: 99%

“…Practitioners using CDSSs experienced more accurate prescribing [ 16 , 24 , 28 , 35 , 38 ], improved screening [ 33 , 37 , 49 , 50 ], improved overall performance [ 21 , 29 , 34 , 44 ], improved care plans [ 17 , 25 , 30 , 47 ], improved documentation [ 31 , 40 ], overall improved buy-in for CDSSs [ 33 , 48 ], increased awareness of needs of patients [ 18 ], improved follow-up with patients due to enhanced communication channels enabled by the application [ 23 ], improved accuracy of diagnosis [ 34 ], and improved benchmarking [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

“…As illustrated, 61% (22/36) of occurrences of themes identified 7 positive patient medical outcomes as a result of using CDSSs [ 15 , 17 , 20 , 21 , 23 , 26 , 27 , 31 , 32 , 34 , 35 , 37 , 39 , 41 - 48 , 50 ]. Patients experienced improved symptoms [ 20 , 23 , 26 , 41 , 43 , 46 - 48 , 50 ], improved feedback from provider [ 17 , 31 , 42 , 45 ], improved disease management [ 21 , 35 , 37 ], improved efficacy of treatment [ 32 , 39 ], improved screening [ 15 , 27 ], and improved safety [ 34 ], and one study even reported improved mortality [ 44 ]. Although 11 articles did not discuss patient medical outcomes [ 16 , 18 , 19 , 24 , 25 , 28 - 30 , 33 , 40 , 49 ], only 3 reported no statistically significant difference in outcomes between control and intervention groups [ 22 , 36 , 38 ].…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

Effects of Computerized Decision Support Systems on Practitioner Performance and Patient Outcomes: Systematic Review

Kruse¹,

Ehrbar²

2020

JMIR Med Inform

View full text Add to dashboard Cite

Background Computerized decision support systems (CDSSs) are software programs that support the decision making of practitioners and other staff. Other reviews have analyzed the relationship between CDSSs, practitioner performance, and patient outcomes. These reviews reported positive practitioner performance in over half the articles analyzed, but very little information was found for patient outcomes. Objective The purpose of this review was to analyze the relationship between CDSSs, practitioner performance, and patient medical outcomes. PubMed, CINAHL, Embase, Web of Science, and Cochrane databases were queried. Methods Articles were chosen based on year published (last 10 years), high quality, peer-reviewed sources, and discussion of the relationship between the use of CDSS as an intervention and links to practitioner performance or patient outcomes. Reviewers used an Excel spreadsheet (Microsoft Corporation) to collect information on the relationship between CDSSs and practitioner performance or patient outcomes. Reviewers also collected observations of participants, intervention, comparison with control group, outcomes, and study design (PICOS) along with those showing implicit bias. Articles were analyzed by multiple reviewers following the Kruse protocol for systematic reviews. Data were organized into multiple tables for analysis and reporting. Results Themes were identified for both practitioner performance (n=38) and medical outcomes (n=36). A total of 66% (25/38) of articles had occurrences of positive practitioner performance, 13% (5/38) found no difference in practitioner performance, and 21% (8/38) did not report or discuss practitioner performance. Zero articles reported negative practitioner performance. A total of 61% (22/36) of articles had occurrences of positive patient medical outcomes, 8% (3/36) found no statistically significant difference in medical outcomes between intervention and control groups, and 31% (11/36) did not report or discuss medical outcomes. Zero articles found negative patient medical outcomes attributed to using CDSSs. Conclusions Results of this review are commensurate with previous reviews with similar objectives, but unlike these reviews we found a high level of reporting of positive effects on patient medical outcomes.

show abstract

Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 62%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Effects of Computerized Decision Support Systems on Practitioner Performance and Patient Outcomes: Systematic Review

Kruse¹,

Ehrbar²

2020

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

Towards Pain-Fingerprinting: A Ubiquitous and Interoperable Clinical Decision Support System for Pain Assessment

Pombo

García

2019

IFMBE Proceedings

View full text Add to dashboard Cite

A novel clustering-based purity and distance imputation for handling medical data with missing values

Cheng

Huang

2021

Soft Comput

View full text Add to dashboard Cite

Nowadays, people pay increasing attention to health, and the integrity of medical records has been put into focus. Recently, medical data imputation has become a very active field because medical data usually have missing values. Many imputation methods have been proposed, but many model-based imputation methods such as expectation-maximization and regression-based imputation based on the variables data have a multivariate normal distribution, which assumption can lead to biased results. Sometimes this becomes a bottleneck, such as computationally more complex than model-free methods. Furthermore, directly remove instances with missing values, this approach has several problems, and it is possible to lose the important data, produce ineffective research samples, and cause research deviations, and so on.Therefore, this study proposes a novel clustering-based purity and distance imputation method to improve the handling of missing values. In the experiment, we collected eight different medical datasets to compare the proposed imputation methods with the listed imputation methods with regard to the results of different situations. In imputation measures, the area under the curve (AUC) is used to evaluate the performance of the imbalanced class datasets in MAR and MCAR experiments, and accuracy is applied to measure its performance of the balanced class in MNAR experiment. Finally, the root-mean-square error (RMSE) is also used to compare the proposed and the listing imputation methods. In addition, this study utilized the elbow method and the average silhouette method to find the optimal number of clusters for all datasets. Results showed that the proposed imputation method could improve imputation performance in the accuracy, AUC, and RMSE of different missing degrees and missing types.

show abstract

Combining Data Imputation and Statistics to Design a Clinical Decision Support System for Post-Operative Pain Monitoring

Cited by 7 publications

References 18 publications

Effects of Computerized Decision Support Systems on Practitioner Performance and Patient Outcomes: Systematic Review

Effects of Computerized Decision Support Systems on Practitioner Performance and Patient Outcomes: Systematic Review

Towards Pain-Fingerprinting: A Ubiquitous and Interoperable Clinical Decision Support System for Pain Assessment

A novel clustering-based purity and distance imputation for handling medical data with missing values

Contact Info

Product

Resources

About