Analysis of Cancer Data from NCDB
The National Cancer Database maintains data on different forms of cancer as provided by different organizations that deal with the condition in the country. NCDB’s database allows the public to access this data and web-based benchmarking applications allow access by the public, clinicians, and researchers. The American College of Surgeons provides the database. In completing the task, a Benchmark Report was created for colon cancer with the variables of ethnicity, age group, and gender. Therefore, three different results were obtained reflecting the number of cases by age, ethnicity, and gender for ten years (2010 to 2019).
The results show that the prevalence of colon disease is high among people between 50 and 89 across all races, regardless of gender. For white people, males aged between 80 and 89 have the largest disease burden, both males and females, as figure 1 shows. However, among female white people, those aged between 60 and 69 have the largest disease burden. In all the groups, people below 40 and above 90 have the lowest disease burdens for both genders. Based on this review, it is evident that the website is highly reliable as it provides accurate data, which is provided based on the specific groups of people representing the entire American population.
NCDB Completeness Report
The NCDB provides analyzed data that presents the total number of analytic cases, the total number of data items that were evaluated, and the overall performance at the hospital. In addition, the database presents the number of error data items and the number of items that need improvement. In this case, there were two types of follow-up:- short-term and long-term. The total number of cases for long-term follow-up (up to 2017) is 553, while for short-term follow-up (up to 2012) is 341. Therefore, the total number of data items evaluated is 553 for the entire period.
Based on the data, it is possible to evaluate the total data performance at the hospital. First, the overall performance of the hospital can be evaluated using the data. In this case, the hospital’s overall performance can be determined as expressed as a percentage. The hospital provided at least some treatment to 82% of the total patients, 90.91% in terms of the hospital percent out of 341 patients presented at the facility.
Similarly, the hospital provided at least some treatment to 5% based on the benchmark, and 0.36% of the total hospital patients presented for long-term follow-up. In addition, the hospital did not conduct any surgical resection, which is about 1% of the benchmark value. It is worth noting that the hospital’s short-term follow-up shows that for a known surgical reaction and all analytical diagnoses, the benchmark is 0%, which is below the benchmark’s 1% for both. Only the least some treatment provided category is a positive but below the benchmark value. A similar pattern appears in the “long-term follow-up,” where all the subset descriptions except the “at least some treatment were provided for 00, 70” are above the benchmark value.
In essence, 31 out of 341 cases were not provided treatment at the facility in the long-term follow-up. Based on these findings, it is evident that 41 data items have an error, given that they have zero numerators. However, 90.91% of the data items do not have an error, and therefore, they are worth the analysis. Specifically, those with errors fall in the categories of “A known surgical resection was performed at the facility, all analytic diagnoses, and Date of First Recurrence.” (Emmerson & Brown, 2021) Nevertheless, about 8% or 31 data items need improvement because they fall below the benchmark. These categories are “first recurrence, date of last contact or death, and cancer status.
Based on this review, it is evident that the hospital of interest is providing vital services to the community. In particular, the hospital is dealing with cancer of different types. At the same time, the hospital can provide screening, diagnosis, treatment, prevention, and management of the different forms of cancer that patients present. It is worth noting that malignancy is one of the major issues affecting many American communities in the modern world. Therefore, hospitals and other clinical settings should be able to provide holistic and patient-oriented services to the community it serves. Consequently, the hospital of interest can meet most of these demands.
How the Hospital Treated Cancer Report
Physicians must conduct a comprehensive analysis to determine whether the initial diagnostic evaluation and first course of treatment the hospital provides to the patients is concordant with evidence-based national treatment guidelines. After conducting this analysis based on the provided data, it is possible to determine how the hospital treated the specific type of cancer. In this case, the particular type of cancer is rectal, one of the several malignant conditions normally presented at the facility. The hospital is expected to perform seven diagnostic tests and treatments based on the data. These tests and treatments are colonoscopies, CEA PreRx, CBC, CT scan, PET/MRU, KRAS, BRAF, and MMR. On the contrary, the specific treatments provided were surgery, chemotherapy, and radiology.
Secondly, it is possible to use the data to determine the specific diagnostic tests based on the number of patients receiving the services. For the biopsy, the types of diagnosis provided were CT scans and PET/MRI, and 18 out of 20 patients received at least one type of the service. The data also shows that out of the 20 patients in the facility, 15 received a CT scan as a diagnostic procedure. Furthermore, 16 patients received CEA Prelex tumor markers as a diagnostic procedure at the facility. For genetic testing MSI and others, 14 patients were provided with this service out of 20 in the group. Moreover, 19 patients were given labs-CBC and chemotherapy profiles. Third, in terms of first-course treatment, the number of patients who received chemotherapy, radiator, and survey was 15, 11, and 8.
Summary
Survival analysis, the method used to analyze the expected duration of time that a patient lives after diagnosis with a disease, appears to be a good approach to determine a hospital’s performance. In particular, the approach is appropriate for analyzing hospitals’ performance in managing brutal conditions and diseases like cancer (Emmerson & Brown, 2021). Data analysis with survival analysis effectively portrays a comparison between two or more groups for the rate at which a scheduled event such as death occurs and the total number of such events in a given group (Emmerson & Brown, 2021). Survival data normally provides non-negative results, but which subject skews depending on the rate of event occurrence? For example, several events can occur rapidly after an action, such as patient deaths following an operation, or those occurring after a long period, such as life expectancy (Emmerson & Brown, 2021). Moreover, this type of data is prone to censoring due to a variety of reasons.
One can conduct an in-depth analysis of the hospital’s performance and the services provided to the patients. In this case, the hospital of interest has a performance that generally fits the expected extent. Judging from the comparison with the benchmark, it is clear that the hospital needs to improve its performance regarding how it provides services to cancer patients. However, the specific services that the hospital provides to its patients are commendable. The case study demonstrates that the hospital provides multiple diagnostic procedures for cancer patients, ensuring that the physicians provide the most appropriate treatment interventions. In addition, the hospital provides at least three treatment approaches that are critical to the treatment and management of different forms of cancer. Despite these findings, it is important to note that there need for further improvement. Specifically, the hospital must consider conducting a benchmark analysis frequently to determine its performance. Moreover, the hospital should consider expanding its diagnostic and treatment services to cater to the wide range of customers presenting with multiple types of cancer.
Reference
Emmerson, J., & Brown, J. M. (2021). Understanding survival analysis in clinical trials. Clinical Oncology, 33(1), 12-14.