HIM-650: HealthCare Data Management.

 

This HIM-650: Healthcare Data Management course examines health care information resources and their impact on administrative functions, interfaces, data security and integrity, and business processes.

Learners study the following topics within this course:

  • Use of relational database management software to construct tables.
  • Develop forms.
  • Create and execute queries.
  • Design and deploy reports and
  • Advance database concepts to automate contemporary business processes.

Learners are able to distinguish between various network hardware technologies and associated data communications protocols in order to direct how organizations design and implement data networks.

HIM-650: Healthcare Data Management.

What is health care data management?

Health care data management or health information management is the systematic organization of health data in digital form. That is, it involves the compilation of patient data from multiple sources across providers and organizations. In other words, health data management comprises all activities relating to managing health data as a valuable resource. Therefore, it includes acquiring, entering, processing, coding, outputting, retrieving, and storing data gathered in the different areas of health care. It also embraces the validation and control of data according to legal or professional requirements.

5 benefits of health care data management.

Health care data management has various important benefits to health care organizations, medical staff, and patients. These benefits include the following:

1.       Create a comprehensive view of patients, households, and patient groups.

Health care data management enables coders to create a health information system that has composite profiles that provide status and enable predictions.

2.       Improve patient engagement.

Health information systems use efficient health care data management to target patients with reminders and care suggestions that can be relevant for them, based on predictive modeling.

3.       Improve health outcomes.

Health information systems provide a platform to track health trends in certain areas or among specific populations, predict new trends and suggest proactive measures to counter rising health issues.

4.       Business decision-making.

Health care data management provides information that is used by healthcare providers to make better data-driven decisions, such as which types of medical professionals to recruit, what equipment to invest in, or which types of patients to focus on in marketing efforts.

5.       Analyze physician activity.

Health care data management enables healthcare organizations to analyze data on medical practitioners such as success rates, time invested in different treatments and medical decisions, and aligning physicians with the goals of the healthcare organization.

 

Challenges facing health care data management.

The following are the 3 challenges that are facing health data professionals today:

·         Fragmented data.

Health care data management involves storing health care data such as patient data in digital form. This means that the medical data is structured in spreadsheets or databases, images or video files, digital documents, scanned paper documents, or maybe stored in specialized formats such as the DICOM format used for MRI scans.

The downside is that this data is widely duplicated, collected multiple times, and stored in different versions by healthcare providers, public health organizations, insurance bodies, pharmacies, and patients themselves. Therefore, there is no one source of truth for information on patient wellbeing.

·         Changes to data.

Medical data continually changes as do the names, professions, locations, and conditions of patients and physicians. Moreover, patients undertake numerous tests and are administered many types of treatment over the years. This may cause these treatments and medications to evolve over time. Thus, leading to the creation of new types of medical treatment, such as telehealth models which require the creation of new types of data.

·         Regulations and compliance.

Medical data is sensitive and must adhere to government regulations, such as the USA Health Insurance Portability and Accountability Act (HIPAA). Data discovery challenges and poor data quality make it difficult to perform the required audits and meet regulatory requirements. It also limits the diversity of data healthcare providers can use for the benefit of patients.

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