Introduction

Health informatics provides the ability to derive insights from data, however how we arrive to data synthesis requires more complex processes. This overview of health informatics explores the both the systems that support the science, as well as the data science that provides useful information for economics and forecasting healthcare burdens.

The topics introduced include:

  1. Healthcare Information Systems
  2. Healthcare Standards
  3. National Healthcare Data
  4. How to Analyze a specific dataset utilizing a combination of datasets, queries, and data science methodologies

Many datasets are available within the healthcare industry, ranging from global and national, to regional and local healthcare systems. The healthcare community utilizes a combination of information systems and standards to promote interoperability for exchanging information electronically.

Healthcare Information Systems and Interoperability

Within the healthcare industry, information systems that manage data are required to securely exchange data with other systems using privacy and security standards. Healthcare information exchange occurs between systems while valuing security, safe, timely, and efficiency for the provision of patient-centered care. Interoperability considers secure data exchange between systems, structural format and syntax of data exchange, common underlying models and codification, as well as organizational standards, policies, and workflows.

Healthcare information systems are protected by laws and regulations, such as within the United States, the Health Insurance Portability and Accountability Act (HIPAA) of 1996 was created to protect sensitive medical records and personal health information for individuals. The HIPAA Security Rule of 2005 additionally introduced additional technical and non-technical safeguards. The General Data Protection Regulation (GDPR) provides privacy and security regulations for processing and storage of data relating to data within the European Union (EU).

Healthcare Standards

Healthcare standards are developed by Standards Development Organizations (SDOs) to meet industry needs, and are often accredited by American National Standards Institute (ANSI).1

The members of SDOs include a variety of healthcare providers, insurance, to technical and many others. The utilization of standards varies upon the mission or interests of the organization, and currently there are over 40 SDOs that develop, bundle Integrating the Healthcare Enterprise (IHE) profiles, and maintain these standards. The development of standards may take two to three years to ensure they work properly and the following diagram shows the process steps for developing standards.1

A directory of SDOs by Industry can be reviewed at the Standards Portal, which provides an abundant amount of information about SDOs including industry sectors, classification by International Classification for Standards (ICS), and other characteristics.2

SDO Categories

SDOs are widely used within the healthcare industry, and typically fall into one of four categories, which include privacy and security standards, terminology, content, as well as data exchange/transport standards. A few examples of each of these include:

Privacy and Security Standards

  • Protected Health Information (PHI) and Electronic (ePHI)
  • Family Educational Rights and Privacy Act (FERPA)
  • HIPAA
  • GDPR

Transport standards

  • Direct Standard: secure message exchange for authenticated, encrypted health information
  • Fast Healthcare Interoperability Resources (FHIR): Application Programming Interface (API) for patient data
  • Digital Imaging and Communications in Medicine (DICOM): medical images
  • Clinical Data Interchange Standards Consortium (CDISC): clinical trial data exchange
  • Prescription data exchange (SCRIPT): electronic prescription data Content standards
  • Consolidated CDA (C-CDA): clinical documents
  • HL7V2 and V3: clinical messaging
  • United States Core Data for Interoperability (USCDI): health data classes and constituent data elements

Terminology Standards

  • Current Procedural Terminology (CPT): codes are numbers assigned to each task and service that you can get from a healthcare provider
  • Healthcare Common procedure Coding System (HCPCS): standardized codes that represent medical procedures, supplies, products and services
  • Systematize Nomenclature of Medicine (SNOMED): comprehensive health and clinical terminology that covers most of the needs of health care documentation
  • National Drug Code (NDC): a universal product identifier for human drugs in the United States
  • International Classification of Disease (ICD-10-CM): promote international comparability in the collection, processing, classification, and presentation of mortality statistics
  • Logical Observation Identifiers Names and Codes (LOINC): clinical terminology that is important for laboratory test orders and results
  • RXNORM: normalized naming system for generic and branded drugs (now includes the United States Pharmacopeia (USP) Compendial Nomenclature from the United States Pharmacopeial Convention. USP is a cumulative data set of all Active Pharmaceutical Ingredients (API)3

A Common Healthcare Framework to Provide Insights for Healthcare Conditions

Healthcare information systems and standards provide the ability to exchange data. Data can also be retrieved to analyze meaningful insights for our future. This next section will explore influenza, known as the flu, which is a common respiratory illness that infects the nose, throat, and lungs. The two primary types, A and B, occur seasonally and flu vaccinations are common to protect ourselves from spreading this risk.

The disease burden from disease can be measured by a number of factors, including illnesses, hospitalizations, deaths, as well as economics associated with treating conditions. The Center for Disease Control (CDC) estimates that the flu burden within the United States between 2010 and 2020 has resulted in 9 to 41 million illnesses, 140,000 – 710,000 hospitalizations, and 12,000 – 52,000 deaths.4 The disparity in these results is often difficult for healthcare providers to verify that a patient was positively identified for having the flu when being treated within the healthcare system.

The healthcare system is also associated with economic systems. Although the data and analysis associated with economics related to healthcare costs reside within healthcare systems and providers, economic analysis was performed by a team of researchers in Australia.5 These results applied to a 2015 U.S. population included the following estimates:

  • Total economic burden of influenza to the healthcare system: $11.2 billion ($6.3-$25.3 billion)
  • Direct medical costs: $3.2 billion ($1.5-$11.7 billion)
  • Indirect costs: $8.0 billion ($4.8-$13.6 billion)

Using Data Science and Analytics to Query and Process Datasets

There is an abundant amount of healthcare data available for analysis, however the most challenging aspect is to align the objective with data sources, followed by representing that information as correctly as possible. The ability to analyze data provides the ability for healthcare systems to operate more efficiently for current and future requirements. This data science and analysis process seeks to determine the overall annual impact associated with influenza in association to individual cost, healthcare system (hospital of analysis) financial cost, and mortality rate in 2020.

The data used to perform research in the United States includes:

  1. US Census Bureau population data: total annual reported population data
  2. Centers for Disease Control (CDC) and Prevention, National Center for Immunization and Respiratory Diseases (NCIRD): patient population
  3. National Institute of Health (NIH), National Library of Medicine (NLM): economic and financial impact from healthcare system associated with societal data

Data Preparation

Data preparation includes initially analyzing the catchment population, which is 200,000 individuals for this analysis. The Centers for Disease Control (CDC), Influenza-like Illness Surveillance Network (ILINet)4,6 ILINet consists of outpatient healthcare providers in all 50 states, Puerto Rico, the District of Columbia and the U.S. Virgin Islands reporting approximately 60 million patient visits during the 2019-20 season. Each week, approximately 3,000 outpatient healthcare providers around the country report data to CDC on the total number of patients seen for any reason and the number of those patients with influenza-like illness (ILI) by age group (0-4 years, 5-24 years, 25-49 years, 50-64 years, and ≥65 years).

Data Sampling

The outcomes of the most importance was to sample the overall data to determine the total effect from annual influenza. The healthcare data provides the ability to separate and isolate specific age groups from the population, and further analysis can be conducted to support associated analysis. For this dataset, the ICD-10-CM standard was used and may be coded with other designations to indicate novel/non-novel as a descriptors

Examples of variables that healthcare administrators can code for influenza include:6

Querying the Data

Datasets can be queried utilizing a number of software and database management applications. The analysis performed on these datasets used a combination of applications, including SAS, R, as well as spreadsheet viewers and editors such as Microsoft Excel.

A strategy for diagnosing the flu would initially include evaluating symptoms, vital signs and risk factors to determine if a patient has a high probability of the flu. “Journalism” table:

Additional Considerations

  • Omit codes: not associated with influenza or respiratory illnesses
  • Codes to include: J10.-, J09.X2, J10.-, J11.1, J11.-
  • Number of discharges: quantify the number of discharges associated with influenza codes for patient treatment
  • Rate of discharges: quantify the length of time that patients that are coded with influenza are entering and exiting the healthcare system

Data Results

  • Risk of admission for flu (CDC cumulative incidence rate for all age groups)7: 0.8 per 100,000 population
  • Number at risk: 2,374 (1.19% average risk rate of 200,000 catchment population size)5
  • Hospital expected number of admissions: 49,906
  • The Healthcare Cost and Utilization Project (HCUP) indicates that 7.7% of all emergency department visits are associated with ILI, and 21.5% admitted as an inpatient from the ED.8
  • Number of discharges expected at the hospital of analysis: 2,374 patients will be admitted, and 2,321 will be discharged (from a catchment population of 200,000)
  • Cost per patient: $11,406 (total flu patients/total average burden of cost)
  • Total cost in the hospital of analysis: $27,083,944 (avg. cost per patient * total patients)
  • In-hospital mortality rate: 0.107% (flu patients / mortality patients)
  • Death burden in the hospital of analysis: 53 patients

Conclusion

This analysis utilized many different statistical factors associated with influenza to forecast the overall annual impact. Initially, the United States population was assessed, which provided various ratios that were meaningful to use for a smaller population catchment. Accuracy can be increased by using more specified criteria, such as the geological location and historical data for the healthcare system. In general, this was a great opportunity to work through multiple datasets to determine conditions associated with influenza using data science and analytics.

Although the original analysis was performed on another software platform, the subsequent objective with this dataset is to transform the file types for machine readability in R, including data visualizations and analysis in a later iteration.

Similar types of work can be performed on many diseases to more accurately determine and forecast healthcare industry trends.

References and Works Cited

  1. “Standards development organizations,” Chapter 2 - SDO Education Playbook. [Online]. Available: https://www.healthit.gov/playbook/sdo-education/chapter-2/.
  2. “Resources,” Standards Developing Organizations (SDOs). [Online]. Available: https://www.standardsportal.org/usa_en/resources/sdo.aspx.
  3. “Rxnorm Overview,” U.S. National Library of Medicine. [Online]. Available: https://www.nlm.nih.gov/research/umls/rxnorm/overview.html.
  4. “Disease burden of flu,” Centers for Disease Control and Prevention. [Online]. Available: https://www.cdc.gov/flu/about/burden/index.html.
  5. “U.S. Influenza Surveillance System: Purpose and Methods.” Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 6 Oct. 2020, www.cdc.gov/flu/weekly/overview.htm.
  6. Putri W.C.W.S., Muscatello D.J., Stockwell M.S., Newall A.T. (2018). Economic burden of seasonal influenza in the United States. Vaccine. 2018 Jun 22;36(27):3960-3966. doi: 10.1016/j.vaccine.2018.05.057. Epub 2018 May 22. PMID: 29801998.
  7. L. M. Johnson, “ICD-10 coding for the flu,” MedLearn Publishing, 19-Oct-2022. [Online]. Available: https://icd10monitor.com/icd-10-coding-for-the-flu/.
  8. “Laboratory-confirmed influenza hospitalizations,” Centers for Disease Control and Prevention. [Online]. Available: https://gis.cdc.gov/grasp/fluview/fluhosprates.html.
  • Data source URLS are subject to change depending upon the access date of accessing URL ** Cover Image credit: Beards, Unsplash

Keywords Analytics, American National Standards Institute (ANSI), Center for Disease Control (CDC), Clinical Data Interchange Standards Consortium (CDISC), Consolidated CDA (C-CDA), Current Procedural Terminology (CPT), data analysis, data preparation, data relationships, data sampling, data science, data sets, data queries, data visualization, Digital Imaging and Communications in Medicine (DICOM), Direct Standard, Family Educational Rights and Privacy Act (FERPA), Fast Healthcare Interoperability Resources (FHIR), General Data Protection Regulation (GDPR), Healthcare Common procedure Coding System (HCPCS), healthcare data, Health Level Seven International (HL7) healthcare information systems, Healthcare Cost and Utilization Project (HCUP), healthcare standards, healthcare utilization, illness, influenza, International Classification of Disease (ICD-10-CM), Logical Observation Identifiers Names and Codes (LOINC), National Drug Code (NDC), population, National Institute of Health (NIH), National Library of Medicine (NLM), Prescription data exchange, Privacy and Security Standards, Protected Health Information (PHI) and Electronic (ePHI), RXNORM, Standards Development Organizations (SDOs), statistics, Systematize Nomenclature of Medicine (SNOMED), Transport standards, U.S. Census Bureau, United States Core Data for Interoperability (USCDI), U.S. Department of Commerce, U.S. Environmental Protection Agency, Health Insurance Portability and Accountability Act (HIPAA).

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