GAP ANALYSIS

the greater accra regional hospital - gap analyis

Godfred Makumator

2/7/2024

’By focusing on 'meaningful use,' we recognize that better healthcare does not come solely from the adoption of technology itself, but through the exchange and use of health information to best inform clinical decisions at the point of care.’’

Jerome Osheroff MD, former chief clinical informatics officer

overview

Introduction

Business Problem

Current and Future state

Gaps between the states

Limitations of Gaps

impacted Stakeholders

Recommendations

Benefits of Projects

Cost of Projects

Conclusion

References

Attachments

Greater Accra Regional hospital

The Greater Accra Regional Hospital (GAHR) stands as one of Ghana's premier healthcare facilities, boasting a bed capacity of approximately 500 and equipped with cutting-edge medical technology. The hospital attends to an average of over 700 patients daily, addressing a spectrum of health conditions from infectious diseases to cancer and lifestyle-related ailments like diabetes.

The diverse range of diseases encountered by doctors at GAHR poses a notable challenge, particularly for both inexperienced and seasoned practitioners, in adhering to the latest treatment modalities and approaches. This challenge has resulted in the recurrence of infections and suboptimal prognoses, as treatments often deviate from current guidelines.

• Addressing illnesses is growing even more intricate as diseases evolve in complexity and exhibit adaptability to diverse medications and treatment methodologies. Despite these challenges, it remains imperative to furnish clinicians with cutting-edge tools, ensuring their capability to administer current and up-to-date treatments as necessitated (Dolgova and Lao 2018).

Introduction

In healthcare, the key challenge is improving how we treat diseases, as it greatly affects our business. The rise in readmission rates, escalating healthcare costs, and, in some instances, patient mortality are direct outcomes of ineffective treatment approaches GARH faces. The demanding workload and patient volume make it exceptionally difficult for healthcare professionals to stay abreast of all the latest treatment modalities for all diseases. About one in five patients comes back, either for a change in medication or further investigation. This is clearly indicative for a better and more sustainable approach to improve treatment outcomes. The GARH is currently in pursuit of alternative approaches that enhance the doctors approach to diseases and keep them updated with the latest treatment guidelines.

Business problem

• Improved prognosis and outcomes reducing complications of diseases.
• Safe medication prescription and patient safety.
• Reduced readmission rates.
• Decrease cost of care by 70% of previous cost.
• Decrease pressure and stress on both human resources and in fractures.

Current State

• Poor prognosis due to wrong or failed diagnosis since clinicians depend on personal knowledge and experiences.
• Limited access to current guidelines causing inconsistencies in patient care.
• No reminders or alerts on missed investigations or guidelines.
• Serious complications such as death.
• Increase in cost of care (2X or 3X).
• Medical malpractice claims.

Future State

Objectives

• Standardization of care and treatments.
• Patient safety due to errors and failure to use standardize treatments.
• Data- driven decision support tools can tailor treatments.
• Higher readmissions rates.
• Increase workload on clinicians.
• Medical malpractice claims.

gaps identified

limitations of gap

1. Cost of system: this new system can be expensive to implement and may require substantial updates every now and then (Sutton and Pincock 2020).

2. Training of clinicians: Due to the already existing workload on clinicians, gathering then for training can be challenging.

3. Due to the already existing workload on clinicians, gathering then for training can be challenging (Sutton and Pincock 2020).

4. Resistance to change as some clinicians might feel their decision making is taking away from them.

STAKEHOLDERS

Direct

• Physicians.
• Nurses.
• Pharmacists.
• Health Informatics Unit.

Indirect

• Hospital Board of directors’
• Administration.
• Health ministry and the public health units (provide guidelines).
• Pharmacy council.
• Finance department.

Appendix

RECOMMENDATION

A highly effective solution for healthcare professionals seeking real-time treatment guidelines, medication error alerts, and comprehensive information on diseases and investigations is the integration of a Clinical Decision Support System (CDSS) into their existing Electronic Health Record (E.H.R.) (Shahmoradi et al. 2021). This system will not only provide the clinicians with the guidelines suitable for a disease, but it will also provide them with the best treatment suitable base on the patients’ medical history.

THE 5S TOOL

To incorporate this system into the existing E.H.R., we will utilize the Lean quality improvement tool known as 5S (Kanamori et al. 2015). The 5S process streamlines checks and balances. The choice of 5S is based on its simplicity, which is crucial given the fast-paced nature of the healthcare system, where workers have limited time. This tool will help us design a CDSS that can (Kanamori et al. 2015).

To incorporate this system into the existing E.H.R., we will utilize the Lean quality improvement tool known as 5S (Kanamori et al. 2015). The 5S process streamlines checks and balances. The choice of 5S is based on its simplicity, which is crucial given the fast-paced nature of the healthcare system, where workers have limited time. This tool will help us design a CDSS that can (Kanamori et al. 2015).

Hypotheses

5. Cost savings and reduced readmission rate (Jacob et al. 2017).

PROJECTED BENEFITS

The benefit of implementing a clinical decision support system into the E.H.R. are;

Enhanced patient safety

Improved patient outcomes

3. Optimized and personalized treatment plans (Jacob et al. 2017).

4. Efficiency and improved workflow and reduced workload on physicians.

The cost of implementing a new CDSS into the E.H.R entails a loss of factors. These factors include;

The estimated cost of the CDSS is $373 000 (GH4 778 130). This cost will comprise of all the factors mentioned above and will also cater for any miscellaneous (Lewkowicz, Wohlbrandt, and Boettinger 2020).

PROJECTED COSTS

• Cost of upgrading the already existing Computers.
• Increasing the cloud storage subscription.
• Improving the internet speed for faster responses.
• Cost of training
• Software and vendor services.

Methodology

In summary, the suboptimal results associated with diseases, heightened rates of readmission, and elevated complications due to inadequate disease management underscore the need for change. The implementation of the new Clinical Decision Support System (CDSS) promises a significant improvement in these factors, signaling a transformative impact on the overall functioning of the General Hospital. Recognized as a pivotal institution in the country, the Greater Accra Regional Hospital stands to benefit immensely from this technological advancement, enhancing its reputation and empowering it to deliver optimal care to the citizens.

CONCLUSIONS

Dolgova, Olga, and Oscar Lao. 2018. “Medicine in the Light of Evolution.” Genes 10, no. 1 (December): 3. https://doi.org/10.3390/genes10010003. Jacob, Verughese, Anilkrishna B Thota, Sajal K Chattopadhyay, Gibril J Njie, Krista K Proia, David P Hopkins, Murray N Ross, Nicolaas P Pronk, and John M Clymer. 2017. “Cost and Economic Benefit of Clinical Decision Support Systems for Cardiovascular Disease Prevention: A Community Guide Systematic Review.” Journal of the American Medical Informatics Association 24, no. 3 (January): ocw160. https://doi.org/10.1093/jamia/ocw160. Kanamori, Shogo, Seydou Sow, Marcia C. Castro, Rui Matsuno, Akiko Tsuru, and Masamine Jimba. 2015. “Implementation of 5S Management Method for Lean Healthcare at a Health Center in Senegal: A Qualitative Study of Staff Perception.” Global Health Action 8, no. 1 (April): 27256. https://doi.org/10.3402/gha.v8.27256. Lewkowicz, Daniel, Attila Wohlbrandt, and Erwin Boettinger. 2020. “Economic Impact of Clinical Decision Support Interventions Based on Electronic Health Records.” BMC Health Services Research 20, no. 1 (September). https://doi.org/10.1186/s12913-020-05688-3. Shahmoradi, Leila, Reza Safdari, Hossein Ahmadi, and Maryam Zahmatkeshan. 2021. “Clinical Decision Support Systems-Based Interventions to Improve Medication Outcomes: A Systematic Literature Review on Features and Effects.” Medical Journal of the Islamic Republic of Iran 35, no. 27 (April). https://doi.org/10.47176/mjiri.35.27. Sutton, Reed, and David Pincock. 2020. “An Overview of Clinical Decision Support Systems: Benefits, Risks, and Strategies for Success.” NPJ Digital Medicine 3, no. 1 (February): 1–10. https://doi.org/10.1038/s41746-020-0221-y.

REFERENCES

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