The growing concern for health in America, after Covid 19, has resulted in a call for healthcare reform in the US. Currently, there has been an increase in privatization of hospitals, as the cost of maintaining large government-run hospitals has become too large. It was recently discovered by a research survey conducted on Private Hospital Market size & share that private hospitals in the US were worth 820 billion in 2021 with an expected annual 12.5% growth rate till 2028 spending approx 2000 billion dollars. This rise of hospital privatization can be attributed to the increase in hospital revenue as non-government funded hospitals need not take in patients under medicare. In a research study done by Stanford University, it was found that “ a formerly government-run hospital admitted on average 15 percent fewer Medicaid patients in the years immediately following privatization.” The result of such a decrease in patients, especially those in poorer communities has created an inequitable distribution of healthcare to those in America. A Pew Research Center survey, asking those who are in rural, urban, and suburban areas about their access to healthcare reported, 23% of Americans in rural areas believe a lack of health access is a problem in their community, compared with 18% of urbanites and 9% of suburbanites. As such, if one could inform citizens of their closest and most accessible hospitals for their needs, more people, especially those in rural areas, could get the help they need.

# healthcare reform

import pandas as pd
df_all_hospital_information = pd.read_csv('Hospitals.csv')
columns_1 = ['X', 'Y', 'NAME', 'STATE', 'ZIP', 'OWNER' ]
df_hospital_information = df_all_hospital_information[columns_1]
df_hospital_information = df_hospital_information.to_dict()

df_all_hospital_quality = pd.read_csv('Hospital General Information.csv', encoding='cp1252')
columns_2 = ['Hospital Name', 'ZIP Code', 'Hospital Type', 'Hospital overall rating', 'Mortality national comparison', 'Patient experience national comparison']
df_hospital_quality = df_all_hospital_quality[columns_2]
df_hospital_quality = df_hospital_quality.to_dict()

I will analyze the given hospitals in both data frames, specifically the quality of care, type of care, and distance from the person who needs help. Using machine learning cluster and prediction methods, based on user input of their injury and location, I will provide for them the closest three hospitals that are best suited and safest for their care that will allow those to gain the necessary care their need who would otherwise are unaware of where to get help.