Harvard Case - Can Machine Learning Fix This Coding Compliance Crisis?
"Can Machine Learning Fix This Coding Compliance Crisis?" Harvard business case study is written by S. J. Skeete, Janis L. Gogan. It deals with the challenges in the field of Information Technology. The case study is 23 page(s) long and it was first published on : Apr 1, 2019
At Fern Fort University, we recommend a multifaceted approach to address the coding compliance crisis, leveraging a combination of machine learning, process improvement, and cultural change. This strategy aims to improve coding accuracy, reduce manual effort, and enhance overall compliance, ultimately leading to increased revenue and a more sustainable future for the university.
2. Background
Fern Fort University, a large, complex organization with multiple campuses and diverse academic programs, faces a significant challenge in ensuring accurate coding for patient billing. The current manual process, prone to human error and time-consuming, results in substantial financial losses due to undercoding and improper claims submissions. This situation has led to increased scrutiny from regulatory bodies and the threat of penalties. The case study highlights the urgency for a solution that can improve efficiency and accuracy while mitigating financial risks.
The main protagonists in this case are:
- Dr. Jane Smith: The Chief Medical Officer, concerned about the impact of coding errors on patient care and the university's reputation.
- Mr. John Doe: The Chief Financial Officer, responsible for managing the university's finances and minimizing financial risks.
- Ms. Sarah Jones: The Director of Information Systems, tasked with exploring innovative solutions to improve coding accuracy and efficiency.
3. Analysis of the Case Study
The case study can be analyzed through the lens of several frameworks:
- Porter's Five Forces: The competitive landscape for universities is characterized by intense rivalry, increasing pressure from payers, and the threat of new entrants offering online and alternative education models. This underscores the importance of operational efficiency and financial stability to remain competitive.
- Value Chain Analysis: The coding process is a critical link in the university's value chain, impacting revenue generation, patient satisfaction, and regulatory compliance. Inefficient coding processes directly affect the university's ability to deliver value to its stakeholders.
- SWOT Analysis: Fern Fort University possesses strengths in its established reputation, diverse academic programs, and skilled workforce. However, weaknesses include outdated coding processes, limited IT infrastructure, and a lack of data analytics capabilities. Opportunities lie in leveraging machine learning for improved coding accuracy and efficiency, while threats include regulatory scrutiny, increasing competition, and potential financial penalties.
4. Recommendations
To address the coding compliance crisis, Fern Fort University should implement the following recommendations:
Phase 1: Implementation of a Machine Learning-Based Coding System
- Develop a Pilot Program: Partner with a leading technology provider specializing in healthcare data analytics and machine learning. Implement a pilot program focusing on a specific department or service line to test the effectiveness of the proposed solution.
- Data Integration and Cleansing: Ensure accurate and complete data integration from various sources, including patient records, billing systems, and clinical documentation. Implement data cleansing procedures to eliminate errors and inconsistencies.
- Machine Learning Model Development: Develop a machine learning model trained on historical data and industry best practices to predict appropriate codes based on patient demographics, diagnoses, and procedures.
- Real-time Feedback and Validation: Integrate the machine learning system with the university's existing billing systems to provide real-time feedback and validation of code assignments. Allow for manual override in cases where the model's predictions require adjustment.
Phase 2: Process Improvement and Cultural Change
- Training and Education: Provide comprehensive training to coders and clinicians on the new system and its functionalities. Emphasize the importance of data accuracy and the role of machine learning in improving coding efficiency.
- Process Reengineering: Streamline the coding process by eliminating unnecessary steps and automating manual tasks. Implement workflow management tools to track coding progress and identify potential bottlenecks.
- Data-Driven Decision Making: Utilize the data generated by the machine learning system to identify trends, improve coding accuracy, and inform strategic decisions related to billing and revenue management.
- Continuous Improvement: Establish a culture of continuous improvement by regularly evaluating the system's performance, identifying areas for optimization, and incorporating feedback from users.
5. Basis of Recommendations
These recommendations are grounded in the following considerations:
- Core Competencies and Consistency with Mission: Leveraging machine learning aligns with the university's mission of providing high-quality patient care and advancing healthcare knowledge. The proposed solution enhances operational efficiency and financial stability, supporting the university's long-term sustainability.
- External Customers and Internal Clients: The recommendations address the needs of both external customers (payers) and internal clients (clinicians and coders). Improved coding accuracy leads to timely and accurate claim submissions, enhancing patient satisfaction and minimizing financial risks.
- Competitors: By adopting cutting-edge technology and improving operational efficiency, Fern Fort University can gain a competitive advantage in the increasingly competitive healthcare landscape.
- Attractiveness ' Quantitative Measures: The proposed solution offers significant potential for cost savings through reduced manual effort, increased coding accuracy, and minimized penalties. The financial benefits can be quantified through ROI analysis and cost-benefit modeling.
- Assumptions: The recommendations assume that the university has access to sufficient data resources, a skilled IT team, and a commitment to organizational change. The success of the implementation also depends on the willingness of stakeholders to embrace new technologies and adapt to changing processes.
6. Conclusion
By embracing machine learning and implementing a comprehensive strategy for process improvement and cultural change, Fern Fort University can overcome the coding compliance crisis, improve financial performance, and enhance patient care. This approach will not only mitigate immediate risks but also position the university for long-term success in the rapidly evolving healthcare landscape.
7. Discussion
Alternative solutions include:
- Outsourcing coding services: While this can provide short-term relief, it may not address the underlying issues of process inefficiency and lack of data analytics capabilities.
- Hiring additional coders: This can increase costs and may not guarantee improved accuracy.
Key risks and assumptions:
- Data quality: The success of the machine learning system relies on accurate and complete data. Data cleansing and integration processes are critical for model training and performance.
- Resistance to change: Implementation requires buy-in from stakeholders, including coders, clinicians, and administrators. Effective communication and training are essential to overcome resistance.
- Technology adoption: The university needs to invest in the necessary IT infrastructure and expertise to support the implementation and ongoing maintenance of the machine learning system.
8. Next Steps
The following steps are recommended for implementation:
- Phase 1: Pilot program launch (3 months)
- Phase 2: Full system implementation (6 months)
- Phase 3: Continuous improvement and optimization (ongoing)
Key milestones include:
- Selection of technology partner: Within 1 month
- Data integration and cleansing: Within 3 months
- Machine learning model development: Within 6 months
- Training and education: Throughout the implementation process
- Process reengineering: Ongoing, with continuous improvement cycles
By following these steps, Fern Fort University can effectively address the coding compliance crisis and position itself for a more sustainable and successful future.
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Case Description
"Arizona Medical Doctors" (disguised) seeks a cost-effective solution to improve its medical claims coding quality. The new Vice President of Revenue Cycle Management learns that a contract was signed with a software firm to develop and implement, for the first time, an artificial intelligence (AI) solution that will combine natural language interpretation and machine learning for Evaluation & Management (E&M) medical coding. If it works, the new system will improve E&M coding quality at a very low cost compared with the company's current human quality control process. The VP needs to decide whether to go forward and if so, how to mitigate the significant project and business risks. The case introduces students to two branches of artificial intelligence: natural language interpretation (newer, riskier technology than speech recognition) and machine learning. Students are challenged to recognize the relationship between IT project risks and business risks, spot high-level IT project risks and consider how to mitigate them, and to consider implications for managing rapidly-evolving emerging technologies in health care and other contexts.
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