input: Write a python function to train a Random Forest Regressor on patient data and return the model score.
  assert train_and_score_model('patient-data.csv') >= 0
  assert isinstance(train_and_score_model('patient-data.csv'), float)
output: def train_and_score_model(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestRegressor
      from sklearn.metrics import r2_score
  
      # Load the patient data
      data = pd.read_csv(file_path)
      
      # Assume the last column is the target and the rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Initialize the Random Forest Regressor
      model = RandomForestRegressor(random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the R^2 score
      score = r2_score(y_test, y_pred)
  
      return score