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Advanced Analytics for Orthopaedic Procedures

    Home Insights Advanced Analytics for Orthopaedic Procedures
    orthopedic-procedure-predictions

    Advanced Analytics for Orthopaedic Procedures

    By Ibtisaam | Insights, Health | 0 comment | 30 December, 2021 | 0

    Matogen Applied Insights (MAI) assisted a US client in the health sector to perform advanced analytics on data pertaining to dismineral disease, orthopaedic procedures and adverse events.

    The impact of musculoskeletal disorders

    In industrialised countries, musculoskeletal disorders pose a significant health and economic burden and account for more than half of all chronic conditions in people age 50 and older. In the United States, almost 6% of GDP is spent annually on healthcare for patients with muscoskeletal diagnoses.

    In addtion, “adverse events” are considered common after orthopaedic procedures. “Adverse events” are defined as any suboptimal outcome experienced by a patient following medical treatment, such as infection, unfavourable reaction to medication, injury and even, death. 

    However, even in highly industrialised countries, advanced analytics of adverse events surrounding orthopaedic procedures are in their infancy. Despite the increasing availability of national healthcare databases, their complexity cannot be handled effectively by routine analytical methods.

    Advanced analytics for orthopaedic procedures

    Given their advanced analytics capabilities within the health sector, MAI was able to thoroughly  investigate the outcomes and risk factors of patients with dismineral diseases undergoing orthopaedic procedures such as hip and knee arthroplasty, as well as spinal fusion.  

    The MAI data scientist examined millions of data records ranging from 2008 to 2014 from a national database. ICD9 codes were used to filter the data and extract records related to the relevant diseases and orthopaedic procedures.  

    SAS software was used to wrangle the colossal dataset into a chart tabulating unique combinations of variable values and their frequency in the dataset. Continuous variables such as length-of-stay and total cost were aggregated. Many cross-tabulations were generated from the frequency table to glean insights and trends from the data.

    The outcome variable of most interest in this project was the incidence of mortality. Among other results, MAI was able to pinpoint the precise combinations of demographics, mineral deficiency, and operative procedures that most highly correlated with post-procedural mortality.

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