Write A 1000-Word Paper Diagnosing Healthcare ✓ Solved

Write a 1000-word paper diagnosing the HealthCare.

Write a 1000-word paper diagnosing the HealthCare.gov launch failures and analyzing the project management mistakes described in the article 'HealthCare.gov Diagnosis: The Government Broke Every Rule Of Project Management'. Relate these failures to principles of relational database design (unary and ternary relationships, referential integrity, delete rules) where relevant. Include in-text citations and a References section with 10 credible sources.

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Executive summary

The October 2013 HealthCare.gov launch exposed a cascade of project management and technical failures that illustrate how unrealistic scope, fragmented authority, inadequate integration, and poor testing produce system collapse (Thompson, 2013; GAO, 2014). Beyond program-level mistakes, database design and data-integrity practices—particularly around complex relationships and referential integrity—contributed to fragility. This paper diagnoses the principal management errors and links them to relational database concepts such as unary/ternary relationships and delete/integrity rules, offering lessons for future large-scale public IT projects.

Diagnosis of project management failures

Contemporary postmortems identify several recurring managerial failures: unrealistic requirements that attempted to consolidate identity management, eligibility determination, plan comparison, and enrollment into a single web flow; extreme technical complexity with many subsystems and contractors; diffuse integration responsibility; fragmented authority inside the agency; inadequate performance metrics; rushed schedules; and insufficient end-to-end testing (Thompson, 2013; GAO, 2014). These failures align with classic software project risk factors: large scope, poor communication, and lack of an authoritative systems integrator (PMI, 2013; Brooks, 1975).

Unrealistic scope produced a brittle architecture. HealthCare.gov had to interact with CMS back-end systems, state exchanges, insurers, identity proofing services, and subsidy-calculation engines — a heterogeneity that magnified interface and data-mapping challenges (GAO, 2014). The decision to retain integration responsibility largely inside CMS rather than hiring a single, accountable lead integrator amplified coordination overhead and left no single entity empowered to run rigorous end-to-end tests (Thompson, 2013).

Fragmented governance worsened the problem. With competing policy, IT, and communications units issuing inconsistent directives, contractors implemented conflicting features or incomplete interfaces. Absent clear metrics and enforcement, schedule pressure drove deployment before full-system verification — a recipe for post-launch firefighting rather than measured delivery (Standish Group, 2013).

Relational-database implications

At the technical layer, the system’s complexity manifested as intricate relational data relationships and heavy reliance on referential integrity across distributed components. Enrollment events linked customers, plans, insurers, subsidies, and identity records — effectively a web of ternary and many-to-many relationships. Modeling such relationships in a single relational schema typically requires join tables (additional relations) for ternary relationships or bill-of-materials–style mappings for many-to-many associations (Elmasri & Navathe, 2015; Date, 2004).

When multiple independent subsystems each enforce their own keys and constraints, two problems arise. First, inconsistent foreign key enforcement across boundaries can permit orphaned records or duplicate identity rows when integration logic fails. Second, distributed delete and update semantics (restrict, cascade, set-to-null) must be carefully designed. For example, if a customer identity record is de-duplicated or removed without coordinating cascade rules across downstream enrollment and subsidy records, enrollments may reference nonexistent keys, producing application errors or loss of eligibility information (Oracle Docs; Elmasri & Navathe, 2015).

HealthCare.gov’s reliance on dozens of contractors and multiple external data sources increased the likelihood of referential mismatches. Without rigorous contract-level data dictionaries and shared referential rules, insert/update anomalies and foreign-key violations become a near certainty. The GAO noted that inadequate end-to-end testing and incomplete interface control resulted in data failures at transaction peaks (GAO, 2014). Those failures are exactly the kinds of symptoms produced by broken referential integrity in distributed relational deployments.

Specific examples linking management errors to DB failures

1) Inadequate testing + complex ternary relations: Ternary relationships such as (customer, plan, subsidy) require that all three keys be consistent during enrollment. If interface mapping for subsidy eligibility is incomplete, insert operations can fail or create partial enrollment rows that violate business rules (Elmasri & Navathe, 2015). End-to-end testing would catch these race conditions before public launch.

2) Fragmented authority + inconsistent delete rules: Different teams or contractors choosing different delete semantics (restrict vs cascade vs set-to-null) can cause either blocked operations or unintended data loss. For example, if an insurer record is removed with a cascade in one system but treated as restricted in another, downstream reconciliation will fail and produce dead-letter queues (Date, 2004).

3) Outsourced integration + weak metrics: Without a single integrator accountable for resolving foreign key domains and reconciliation rules, mismatch resolution devolves into ad hoc scripts and manual fixes under load, which scale poorly during peak enrollment (Brooks, 1975; PMI, 2013).

Lessons and recommendations

From a project-management perspective, the primary remedies are well known: reduce scope for initial releases, appoint a single accountable systems integrator, create unambiguous success metrics, and schedule ample time for end-to-end testing and staged rollouts (PMI, 2013; Standish Group, 2013). Technically, teams should employ canonical data models, shared data dictionaries, and strict interface contracts that define primary/foreign keys and delete/update semantics. Implementing distributed transaction patterns or idempotent APIs with reconciliation workflows reduces the risk of inconsistent referential states across services (Elmasri & Navathe, 2015).

Operationally, use progressive deployment (feature toggles, phased enrollments) to limit exposure, and require simulated peak-load testing with real downstream interfaces where possible. Finally, codify governance for data lifecycle rules (who can delete or change master records and how such actions cascade) to prevent administrative surprises.

Conclusion

HealthCare.gov’s faltering debut combined classic managerial failures with predictable database integrity challenges. Unrealistic scope, fragmented governance, insufficient integration responsibility, and inadequate testing produced a brittle system where complex relational dependencies could not be robustly enforced across many contractors and subsystems (Thompson, 2013; GAO, 2014). Correcting these problems requires both disciplined project management and careful relational-data architecture: canonical models, explicit referential rules, and coordinated delete/update semantics enforced through testing and a single integrator’s authority.

References

• Thompson, L. (2013). HealthCare.gov Diagnosis: The Government Broke Every Rule Of Project Management. Forbes.
• U.S. Government Accountability Office (GAO). (2014). HealthCare.gov: Actions Needed to Address Major Weaknesses. GAO-14-694.
• Project Management Institute. (2013). A Guide to the Project Management Body of Knowledge (PMBOK Guide), 5th ed. PMI.
• Brooks, F. P., Jr. (1975). The Mythical Man-Month: Essays on Software Engineering. Addison-Wesley.
• Standish Group. (2013). CHAOS Report 2013: Project Success and Failure Factors. Standish Group.
• Creswell, J., & Cohen, M. (2013). Problems Plague HealthCare.gov Launch. The New York Times.
• Elmasri, R., & Navathe, S. B. (2015). Fundamentals of Database Systems, 7th ed. Pearson.
• Date, C. J. (2004). An Introduction to Database Systems, 8th ed. Addison-Wesley.
• Oracle Corporation. (n.d.). Oracle Database Documentation: Data Integrity, Constraints, and Referential Integrity. Oracle Docs.
• Centers for Medicare & Medicaid Services (CMS). (2014). HealthCare.gov Post-Launch Stabilization Report. CMS.