Data analytics in health promotion: Health market segmentation and classification of total joint replacement surgery patients

Swenson, Eric R.; Bastian, Nathaniel D.; Nembhard, Harriet Black

doi:10.1016/j.eswa.2016.05.006

Cited by 13 publications

(9 citation statements)

References 21 publications

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“…Market segmentation techniques applied to patient EMR data have shown that patients cluster into a small number of unique segments that share likeness between a subset of patient predictors such as age, LOS, BMI. (Swenson et al, 2016a). We applied the predictive cluster model from Swenson et al (2016a) to our patient data-set and assigned each patient to one of six distinct clusters (i.e., health market segments).…”

Section: Resultsmentioning

confidence: 99%

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Reducing cost drivers in total joint arthroplasty: understanding patient readmission risk and supply cost

et al. 2017

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Introduction: Understanding and planning for the factors that impact supply cost and unplanned readmission risk for total joint arthroplasty (TJA) patients is helpful for hospitals at financial risk under bundled payments. Readmission and operating room supply costs are two of the biggest expenses. Methods: Logistic and linear regressions are used to measure the impacts of TJA patient attributes on readmission risk and supply costs, respectively. Results: Patients' health market segment and the number/type of comorbidity impacts 30/90day readmission rates. Surgeon implant preference and type of surgery impact supply costs. Discharge location and two of the five health market segments increase the odds of 30-day readmission. Arrhythmia and lymphoma are the primary comorbidities that impact the odds of readmission at 90 days. Conclusions: Preoperatively identifying TJA patients likely to have large supply costs and higher readmission risk allows hospitals to invest in low-cost interventions to reduce risk and improve healthcare value.

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Section: Resultsmentioning

confidence: 99%

“…(Swenson et al, 2016a). We applied the predictive cluster model from Swenson et al (2016a) to our patient data-set and assigned each patient to one of six distinct clusters (i.e., health market segments). Table 2 summarises the patient clusters.…”

Section: Resultsmentioning

confidence: 99%

Reducing cost drivers in total joint arthroplasty: understanding patient readmission risk and supply cost

et al. 2017

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“…Furthermore, the use of distance map and dendrogram techniques will allow the study of the behaviour patterns among participants and the differences between them. In summary, the use of these techniques will allow the elaboration of similar programs as well as the development of these techniques in common spaces and/or schedules, which will enable the intervention to be more precise, sustainable, and effective [1][2][3][4][5]. The eEarlyCare computer application has been shown to be effective for the observational recording of the functional abilities of users with different degrees of cognitive disability [11][12][13][14].…”

Section: Discussionmentioning

confidence: 99%

“…To achieve this, supervised and unsupervised learning Data Mining techniques are applied to facilitate prediction, discovery of behavioural patterns, classification, and grouping of users according to different characteristics that are not established a priori. This facilitates the detection of coincidences in assessed groups [4]. These aspects are very important, since they will help professionals with the development of differential diagnoses and the application of personalized therapeutic intervention programs [5].…”

Section: Introductionmentioning

confidence: 98%

Evaluation of Functional Abilities in 0–6 Year Olds: An Analysis with the eEarlyCare Computer Application

Manzanares

Arnaiz‐González

2020

IJERPH

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The application of Industry 4.0 to the field of Health Sciences facilitates precise diagnosis and therapy determination. In particular, its effectiveness has been proven in the development of personalized therapeutic intervention programs. The objectives of this study were (1) to develop a computer application that allows the recording of the observational assessment of users aged 0–6 years old with impairment in functional areas and (2) to assess the effectiveness of computer application. We worked with a sample of 22 users with different degrees of cognitive disability at ages 0–6. The eEarlyCare computer application was developed with the aim of allowing the recording of the results of an evaluation of functional abilities and the interpretation of the results by a comparison with "normal development". In addition, the Machine Learning techniques of supervised and unsupervised learning were applied. The most relevant functional areas were predicted. Furthermore, three clusters of functional development were found. These did not always correspond to the disability degree. These data were visualized with distance map techniques. The use of computer applications together with Machine Learning techniques was shown to facilitate accurate diagnosis and therapeutic intervention. Future studies will address research in other user cohorts and expand the functionality of their application to personalized therapeutic programs.

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“…An AUC score of 1 indicates a perfect classification performance, while an AUC score of 0.5 represents a model that is equivalent to a random guess. Typically, a classifier achieving an AUC value of 0.8 or higher is regarded to be a good model [19,25]. Besides, the statistical significance between the AUC values of ML algorithms is established using the DeLong's method [26].…”

Section: Model Evaluationmentioning

confidence: 99%

A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers

Srinivas

2020

IJERPH

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Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, which, in turn, could lead to patient dissatisfaction, reduced care quality, and physician burnout. However, rescheduling late arrivals could delay access to care. This paper aims to predict the patient-specific risk of late arrival using machine learning (ML) models. Data from two different ambulatory care facilities are extracted, and a comprehensive list of predictor variables is identified or derived from the electronic medical records. A comparative analysis of four ML algorithms (logistic regression, random forests, gradient boosting machine, and artificial neural networks) that differ in their training mechanism is conducted. The results indicate that ML algorithms can accurately predict patient lateness, but a single model cannot perform best with respect to predictive performance, training time, and interpretability. Prior history of late arrivals, age, and afternoon appointments are identified as critical predictors by all the models. The ML-based approach presented in this research can serve as a decision support tool and could be integrated into the appointment system for effectively managing and mitigating tardy arrivals.

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Data analytics in health promotion: Health market segmentation and classification of total joint replacement surgery patients

Cited by 13 publications

References 21 publications

Reducing cost drivers in total joint arthroplasty: understanding patient readmission risk and supply cost

Reducing cost drivers in total joint arthroplasty: understanding patient readmission risk and supply cost

Evaluation of Functional Abilities in 0–6 Year Olds: An Analysis with the eEarlyCare Computer Application

A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers

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