MHA FPX 5064 Assessment 2 Using Data for Decision Making

MHA FPX 5064 Assessment 2 Using Data for Decision Making

Student Name

Capella University

MHA-FPX5062 Healthcare Delivery: New Environments in Health Informatics

Prof. Name

Date

Types of Internal Data Within a Health Care System

Health care organizations rely heavily on internal data to support operational decision-making and ensure high-quality care delivery. Internal data refers to the information that originates within the health care system itself, often captured during patient care or administrative processes. As emphasized by Dooling et al. (2015), understanding both internal and external data sources is crucial for effective data analysis and organizational success.

Categories of Internal Data

Internal data typically encompasses clinical and administrative information systems. Clinical data systems record patient diagnoses, treatment plans, and care outcomes, while administrative systems track financial, operational, and demographic data. These systems form the backbone of data-driven decision-making in hospitals.

Type of Internal Data System	Description	Examples
Clinical Data Systems	Focus on patient health records and care documentation	Electronic Health Records (EHRs), Radiology Information Systems, Laboratory Information Systems
Administrative Data Systems	Manage operational, financial, and demographic information	Patient Financial Systems, Scheduling Databases, Billing Systems
Specialized Registries	Collect specific clinical or disease-related information	Cancer Registry, Immunization Registry

During preadmission and admission, health care institutions gather critical patient information such as demographic details, socioeconomic background, consent forms, and insurance data. This information becomes part of the patient’s Electronic Health Record (EHR) — a comprehensive, digital version of the patient’s medical history that supports continuity of care across departments and facilities (Adler-Milstein et al., 2015).

Internal Data Use in Practice: St. Anthony Medical Center

At St. Anthony Medical Center, internal data plays a vital role in ensuring efficient clinical operations. Departments utilize patient records and administrative data through integrated EHR systems to enhance coordination and patient safety. For instance, the Maternal Care Information System in the Maternal and Fetal Medicine Department and the Prescription Drug Monitoring Program (PDMP) within the pharmacy are both connected to the EHR. These integrated systems promote seamless data sharing, enabling health professionals to make informed, data-driven decisions (Vila Health, n.d.).

Types of External Data Available Within a Health Care System

External data refers to information obtained from sources outside the organization that can be used to benchmark, compare, and improve internal performance. According to Palmer et al. (2019), the ability to acquire, distribute, and interpret external data is essential for enhancing quality and achieving health care excellence.

Examples of External Data Sources

External Data Source	Purpose and Use
The Joint Commission	Evaluates hospital performance and accreditation standards
Centers for Medicare and Medicaid Services (CMS)	Provides quality metrics and reimbursement benchmarks
U.S. Department of Health and Human Services (HHS)	Offers public health and policy-related data
Substance Abuse and Mental Health Services Administration (SAMHSA)	Supplies behavioral health statistics for population comparisons

External data is essential for comparing internal health statistics against broader national or regional trends. Health care organizations often use online databases, research libraries, and national registries to align internal practices with best standards. For example, St. Anthony’s Behavioral Health Department uses SAMHSA data to measure behavioral health outcomes against county, state, and national averages. Similarly, the Human Resources Department analyzes labor market and nursing education data to address workforce planning and recruitment. By integrating external data, St. Anthony promotes a culture of continuous improvement (Crapo, 2015).

Strategies for Accessing and Analyzing Available Data

Integrating diverse data sources is crucial for providing comprehensive, high-quality patient care. However, data storage and sharing practices often differ among organizations, creating challenges in standardization and interoperability. According to Dash (2018), access to reliable data is critical for identifying health disparities and designing equitable interventions.

Data Integration Challenges and Solutions

Challenge	Impact	Strategic Solution
Fragmented data systems	Inconsistent data analysis and poor interoperability	Implement standardized data exchange protocols
Lack of interoperability	Redundant testing and inefficiencies	Adopt integrated systems such as Health Information Exchange (HIE)
Limited access to external datasets	Reduced data-driven insight	Develop partnerships with payers, public health agencies, and government databases

For Vila Health, the integration of data from multiple facilities (e.g., Delaware County Health and Red River Health) ensures accurate, holistic patient records. Collaborative data-sharing with external organizations such as local health departments or payer systems can also enhance community health outcomes.

Data Needs Within a Health Care System

To ensure optimal patient care and operational efficiency, health care organizations must assess their data requirements. Interviews with leaders at St. Anthony Medical Center revealed the pressing need for interoperability among newly acquired facilities’ health information systems.

Identified Data Needs

• Integration of EHRs across facilities

• Comparative analysis using external quality and demographic data

• Standardization of reporting systems

• Real-time data access for providers

Comparing internal data (e.g., patient satisfaction, quality measures) with external data (e.g., regional care quality metrics) helps Vila Health identify gaps in care delivery and prioritize improvement initiatives.

Strategies for Meeting Data Needs

To address identified data gaps, Vila Health should implement an interoperable Health Information Exchange (HIE) system. According to HealthIT.gov (2020), an HIE enables secure, electronic sharing of patient information across multiple providers and facilities, fostering improved care coordination and safety.

Benefits of Health Information Exchange (HIE)

Benefit	Description
Improved decision-making	Provides clinicians with comprehensive patient information
Enhanced care coordination	Reduces redundant testing and minimizes errors
Increased patient safety	Ensures timely access to accurate medical data
Data standardization	Facilitates uniform data formats through CCR and CCD standards

Implementing HIE within Vila Health will promote effective interoperability and enable providers to make more informed, timely, and evidence-based decisions (Wen et al., 2010; Boussadi & Zapletal, 2017).

Communication Strategies for Disseminating Strategic Information to End Users

Effective communication is central to ensuring that vital data reaches all end-users — including clinicians, administrators, and patients. A well-structured communication strategy ensures that relevant health care data is accessible, understandable, and actionable.

According to Dash (2018), evaluation plans should include departmental benchmarks, patient outcomes, and cost-effectiveness indicators to engage users in continuous improvement. However, concerns such as data security, language barriers, and cultural differences may impede effective information exchange.

To address these challenges, The Joint Commission mandates standardized communication protocols to prevent misunderstandings and ensure data security. Health care organizations should assess communication effectiveness regularly and employ technology-driven solutions such as secure messaging, dashboards, and real-time alerts (Thomson et al., 2015).

Conclusion

As Vila Health expands its operations, leadership must ensure that data systems are interoperable, secure, and strategically aligned with organizational goals. The integration of internal and external data supports comprehensive decision-making and enhances patient outcomes. By leveraging advanced health information systems, Vila Health can establish a data-driven culture that prioritizes patient safety, quality care, and operational excellence.

References

Adler-Milstein, J., Everson, J., & Lee, S. D. (2015). EHR adoption and hospital performance: Time-related effects. Health Services Research, 50(6), 1751–1771. https://doi.org/10.1111/1475-6773.12406

Boussadi, A., & Zapletal, E. (2017). A fast healthcare interoperability resources (FHIR) layer implemented over i2b2. BMC Medical Informatics and Decision Making, 17(1), 120–120. https://doi.org/10.1186/s12911-017-0513-6

Crapo, J. (2015). Evaluating demographic data for improving hospital services. Health Data Management Journal, 22(3), 45–52.

Dash, S. (2018). Data across sectors for health datasets available on WPRDC. Targeted News Service.

Dooling, J. A., Houser, S. H., Milaelian, R., & Smith, C. P. (2016). Transitioning to a data-driven, informatics-oriented department. Journal of AHIMA, 87(10), 58–62.

HealthIT.gov. (2020). Health information exchange (HIE). https://www.healthit.gov/topic/health-it-and-health-information-exchange

Palmer, E. L., Higgins, J., Hassanpour, S., Sargent, J., Robinson, C. M., Doherty, J. A., & Onega, T. (2019). Assessing data availability and quality within an electronic health record system through external validation against an external clinical data source. BMC Medical Informatics and Decision Making, 19(1), 143–143. https://doi.org/10.1186/s12911-019-0864-2

Thomson, K., Outram, S., Gilligan, C., & Levett-Jones, T. (2015). Interprofessional experiences of recent healthcare graduates: A social psychology perspective on the barriers of effective communication, teamwork, and patient-centred care. Journal of Interprofessional Care, 29(6), 634–640. https://doi.org/10.3109/13561820.2015.1040873

Vila Health. (2022). Using data for decision making. Capella University. http://media.capella.edu/CourseMedia/VilaHealth/MHA5064/UsingDataForDecisionMaking/transcript.html

Wen, K., Kreps, G., Zhu, F., & Miller, S. (2010). Consumers’ perceptions about and use of the internet for personal health records and health information exchange: Analysis of the 2007 health information national trends survey. Journal of Medical Internet Research, 12(4), e73–e73. https://doi.org/10.2196/jmir.1668