Analysis of Trajectories of Care After Bariatric Surgery Using Data Mining Method and Health Administrative Information Systems

Analytical methods for identifying sequences of utilization in health data: a scoping review

BACKGROUND

Healthcare, as with other sectors, has undergone progressive digitalization, generating an ever-increasing wealth of data that enables research and the analysis of patient movement. This can help to evaluate treatment processes and outcomes, and in turn improve the quality of care. This scoping review provides an overview of the algorithms and methods that have been used to identify care pathways from healthcare utilization data.

METHOD

This review was conducted according to the methodology of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews (PRISMA-ScR) Checklist. The PubMed, Web of Science, Scopus, and EconLit databases were searched and studies published in English between 2000 and 2021 considered. The search strategy used keywords divided into three categories: the method of data analysis, the requirement profile for the data, and the intended presentation of results. Criteria for inclusion were that health data were analyzed, the methodology used was described and that the chronology of care events was considered. In a two-stage review process, records were reviewed by two researchers independently for inclusion. Results were synthesized narratively.

RESULTS

The literature search yielded 2,865 entries; 51 studies met the inclusion criteria. Health data from different countries ([Formula: see text]) and of different types of disease ([Formula: see text]) were analyzed with respect to different care events. Applied methods can be divided into those identifying subsequences of care and those describing full care trajectories. Variants of pattern mining or Markov models were mostly used to extract subsequences, with clustering often applied to find care trajectories. Statistical algorithms such as rule mining, probability-based machine learning algorithms or a combination of methods were also applied. Clustering methods were sometimes used for data preparation or result compression. Further characteristics of the included studies are presented.

CONCLUSION

Various data mining methods are already being applied to gain insight from health data. The great heterogeneity of the methods used shows the need for a scoping review. We performed a narrative review and found that clustering methods currently dominate the literature for identifying complete care trajectories, while variants of pattern mining dominate for identifying subsequences of limited length.

Measuring hospital spatial accessibility using the enhanced two-step floating catchment area method to assess the impact of spatial accessibility to hospital and non-hospital care on the length of hospital stay

Background

Optimal healthcare access improves the health status and decreases health inequalities. Many studies demonstrated the importance of spatial access to healthcare facilities in health outcomes, particularly using the enhanced two-step floating catchment area (E2SFCA) method. The study objectives were to build a hospital facility access indicator at a fine geographic scale, and then to assess the impact of spatial accessibility to inpatient hospital and non-hospital care services on the length of hospital stay (LOS).

Methods

Data concerning older adults (≥75 years) living in the Nord administrative region of France were used. Hospital spatial accessibility was computed with the E2SFCA method, and the LOS score was calculated from the French national hospital activity and patient discharge database. The relationship between LOS and spatial accessibility to inpatient hospital care and to three non-hospital care types (general practitioners, physiotherapists, and home-visiting nurses) was analyzed with linear regression models.

Results

The mean number (standard deviation) of beds per 10,000 inhabitants was 19.0 (10.69) in Medical, Surgical and Obstetrics (MCO) facilities and 5.58 (2.19) in Postoperative and Rehabilitation Care (SSR) facilities, highlighting important variations within the region. Accessibility to hospital services was higher in large urban areas, despite the dense population and higher demand. In 2014, the mean LOS scores were 0.26 for MCO and 0.85 for SSR, but their geographical repartition was non-homogeneous. The linear regression analysis revealed a strong negative and significant association between LOS and non-hospital care accessibility.

Conclusions

This is the first study to measure spatial accessibility to inpatient hospital care in France using the E2SFCA method, and to investigate the relationship between healthcare utilization (LOS score) and spatial accessibility to inpatient hospital care facilities and three types of non-hospital care services. Our findings might help to make decisions about deploying additional beds and to identify the best locations for non-hospital care services. They might also contribute to improve access, and to ensure the best coordination and sustainability of inpatient and outpatient services, in order to better cover the population’s healthcare needs. International studies using multiple consensual indicators of healthcare outcomes and accessibility and sophisticated modeling methods are needed.

A Scoping Review of Artificial Intelligence and Machine Learning in Bariatric and Metabolic Surgery: Current Status and Future Perspectives

Artificial intelligence (AI) is a revolution in data analysis with emerging roles in various specialties and with various applications. The objective of this scoping review was to retrieve current literature on the fields of AI that have been applied to metabolic bariatric surgery (MBS) and to investigate potential applications of AI as a decision-making tool of the bariatric surgeon. Initial search yielded 3260 studies published from January 2000 until March 2021. After screening, 49 unique articles were included in the final analysis. Studies were grouped into categories, and the frequency of appearing algorithms, dataset types, and metrics were documented. The heterogeneity of current studies showed that meticulous validation, strict reporting systems, and reliable benchmarking are mandatory for ensuring the clinical validity of future research.

Efficiency of fine scale and spatial regression in modelling associations between healthcare service spatial accessibility and their utilization

BACKGROUND

Healthcare accessibility, a key public health issue, includes potential (spatial accessibility) and realized access (healthcare utilization) dimensions. Moreover, the assessment of healthcare service potential access and utilization should take into account the care provided by primary and secondary services. Previous studies on the relationship between healthcare spatial accessibility and utilization often used conventional statistical methods without addressing the scale effect and spatial processes. This study investigated the impact of spatial accessibility to primary and secondary healthcare services on length of hospital stay (LOS), and the efficiency of using a geospatial approach to model this relationship.

METHODS

This study focused on the ≥ 75-year-old population of the Nord administrative region of France. Inpatient hospital spatial accessibility was computed with the E2SFCA method, and then the LOS was calculated from the French national hospital activity and patient discharge database. Ordinary least squares (OLS), spatial autoregressive (SAR), and geographically weighted regression (GWR) were used to analyse the relationship between LOS and spatial accessibility to inpatient hospital care and to three primary care service types (general practitioners, physiotherapists, and home-visiting nurses). Each model performance was assessed with measures of goodness of fit. Spatial statistical methods to reduce or eliminate spatial autocorrelation in the residuals were also explored.

RESULTS

GWR performed best (highest R2 and lowest Akaike information criterion). Depending on global model (OLS and SAR), LOS was negatively associated with spatial accessibility to general practitioners and physiotherapists. GWR highlighted local patterns of spatial variation in LOS estimates. The distribution of areas in which LOS was positively or negatively associated with spatial accessibility varied when considering accessibility to general practitioners and physiotherapists.

CONCLUSIONS

Our findings suggest that spatial regressions could be useful for analysing the relationship between healthcare spatial accessibility and utilization. In our case study, hospitalization of elderly people was shorter in areas with better accessibility to general practitioners and physiotherapists. This may be related to the presence of effective community healthcare services. GWR performed better than LOS and SAR. The identification by GWR of how these relationships vary spatially could bring important information for public healthcare policies, hospital decision-making, and healthcare resource allocation.

Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network

The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from a regional coordination network, the Ile-de-France ALS network. This coordination network provides care for 92% of patients diagnosed with ALS living in Ile-de-France. We developed a modular ontology (OntoPaRON) for the automatic processing of these unstructured textual data. OntoPaRON has different modules: the core, medical, socio-environmental, coordination, and consolidation modules. Our approach was unique in its creation of fully defined concepts at different levels of the modular ontology to address specific topics relating to healthcare trajectories. We also created a semantic annotation tool specific to the French language and the specificities of our corpus, the Ontology-Based Semantic Annotation Module (OnBaSAM), using the OntoPaRON ontology as a reference. We used these tools to annotate the records of 928 patients automatically. The semantic (qualitative) annotations of the concepts were transformed into quantitative data. By using these pipelines we were able to transform unstructured textual data into structured quantitative data. Based on data processing, semantic annotations, sociodemographic data for the patient and clinical variables, we found that the need and demand for human and technical assistance depend on the initial form of the disease, the motor state, and the patient age. The presence of exhaustion in care management, is related to the patient’s motor and cognitive state.

Malignancy in bariatric surgery patients: a French multisite cohort

BACKGROUND

Bariatric surgery is associated with decreased cancer-related mortality. An indefinite proportion of patients that undergo bariatric surgery have a history of malignancy or will develop cancer. In these patients, weight loss and oncologic evolution needed to be assessed. The aim of this study was to report the results of patients diagnosed with malignancy before and after bariatric surgery in a French multisite cohort.

METHODS

We conducted a retrospective cohort study of all patients who underwent bariatric surgery in six university centers. Patients were divided in two groups: patients with a preoperative history of malignancy and patients diagnosed with malignancy during the follow-up. Both groups were compared with control groups of patients that underwent surgery during the same period.

RESULTS

From 2008 to 2018, 8927 patients underwent bariatric surgery. In patients with a history of malignancy (n = 90), breast and gynecologic cancers were predominant (37.8%). Median interval between malignancy and surgery was 60 (38-118) months. After a follow-up of 24 (4-52) months, 4 patients presented with cancer recurrence. Comparative analysis demonstrated equivalent weight loss one year after surgery. In patients with postoperative malignancy (n = 32), breast and gynecologic cancers were also predominant (40.6%). Median interval between surgery and malignancy was 22 (6-109) months. In the comparative analysis, weight loss was similar at 2 years.

CONCLUSIONS

History of malignancy should not be considered as an absolute contraindication for bariatric surgery. Gynecological cancer screening should be reinforced before and after surgery. The development of malignancy postoperatively does not seem to affect mid-term bariatric outcomes.