Analyzing missed appointments in medical care is vital because it can help improve patient outcomes and healthcare efficiency. Missed appointments can lead to delayed diagnoses, worsening of medical conditions, and overall increased healthcare costs. By understanding why patients miss appointments, healthcare providers can implement strategies to reduce no-shows, such as sending reminders, offering flexible scheduling, or addressing transportation issues. It's all about enhancing patient care and optimizing the use of healthcare resources.

Enter Bob, an application developer with a keen interest in healthcare technology. Bob was working on an innovative application designed to predict whether a patient was likely to skip their appointment. To test his application, he needed realistic test data in JSON format.

Bob discovered ParroFile, a free online tool that could generate test data in various file formats. ParroFile was particularly adept at handling complex logic using C# expressions, which was exactly what Bob needed

  • PatientId A number that has 12-14 digits
  • AppointmentID A number that is between 5000000 and 6000000
  • Gender Discrete values that are F or M
  • ScheduleDay The day when patient scheduled the appointment
  • ScheduleBaseDay Helper field that represents the schedule base day, which is fixed date 2016/04
  • ScheduleBaseDayIncrementNumber Helper field which is incremental number of days added to helper field ScheduleBaseDay
  • AppointmentDay The day of appointment
  • DaysToAppointment Number of days between schedule and appointment day.
  • Age Age of patient
  • Neighbourhood A discrete values of neighbourhoods
  • Scholarship Whether the patient has scholarship. Based on requirement, the non-scholarship to scholarship ratio is 10:1
  • ScholarshipHelper Helper field of Scholarship
  • Hipertension Whether the patient has hipertension. Based on requirement, the non-hipertension to hipertension ratio is 8:3
  • HipertensionHelper Helper field of Hipertension
  • Diabetes Whether the patient has diabetes. Based on requirement, the non-diabetes to diabetes ratio is 10:1
  • DiabetesHelper Helper field of Diabetes
  • Alcoholism Whether the patient has alcoholism. Based on requirement, the non-alcoholism to alcoholism ratio is 11:1
  • AlcoholismHelper Helper field of Alcoholism
  • Handicap Whether the patient has handicap. Based on requirement, the non-handicap to handicap ratio is 11:1
  • HandicapHelper Helper field of Handicap
  • SMSReceived Has patient received SMS message about appointment. Based on requirement, the SMS-non-received to SMS-received ratio is 7:4.
  • SMSReceivedHelper Helper field of SMSReceived
  • NoShow Does the patient now show up on appointment day. Based on requirement, the ratio of No to Yes is 9:2
  • NoShowHelper Helper field of NoShow
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With these specifications, ParroFile generated the test data accurately. Bob was thrilled with the results. The data was precise and met all his requirements, allowing him to thoroughly test his application.

Satisfied with his work, Bob leaned back in his chair, a sense of accomplishment washing over him. He poured himself a nice cup of tea, savoring the moment.