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Time Motion Study in Integrated Chronic Care Clinic Hannah C. Reiser Loyola University Chicago Stritch School of Medicine Introduction References Methods Results Discussion Conclusion PIH/APZU: Partners In Health (PIH), which is known as Abwenzi Pa Za Umoyo (APZU) in Chichewa, is an NGO that partnered with the Malawi Ministry of Health (MOH) in 2007 to strengthen health services in the southwestern, rural and impoverished district of Neno. PIH's mission is to create a preferential option for the poor in healthcare by accompanying the public sector in strengthening health services, professional training and mentorship, and targeted research. Within the district there are 2 hospitals, a district facility and a community facility, in addition to 13 decentralized health centers. 1 Integrated Chronic Care Clinic: In 2009, PIH/ APZU began enrolling patient’s in what was then known as the Neno Chronic Care Clinic (CCC). This clinic was designed to integrate care for non-communicable diseases (NCDs) with the existing healthcare delivery platform for HIV care. The common NCDs covered included hypertension, asthma, diabetes, epilepsy and congestive heart failure. By late 2014, PIH/ APZU began the process of decentralizing patients for CCC to appointments at nearest health center to their home. 1 Now known as the Integrated Chronic Care Clinic (IC3), the team offers clinic multiple times a week at the district and community hospitals, in addition to biweekly or monthly clinics at each health center seeing 80-200 patients per day. The model of patient flow through IC3 is based on a series of screening and health stations. As PIH/APZU works toward increasing coverage for HIV/AIDS and NCD care in the district, it is important to maximize efficiency and improve patient flow given the human resource shortages. 2 Image 1: Integrated Chronic Care Clinic at Neno District Hospital. 5 IC3 Site Description: Data collection took place at the dedicated IC3 space at Neno District Hospital. This was built to accommodate the health education programs, various screening stations, and one-on-one visits with healthcare providers. Data Collection Training: Data were trained during the pilot phase of data collection. A clinic- wide training took place with the entire staff. Design: We designed a prospective, cross- sectional time-motion study using clerk-generated timestamps. 4 Clerk-generated Timestamps: Upon arrival at clinic, 20% of patients were randomly given a Patient Data Collection Slip. At each of these described stations, the clerk administering the service logged start and end times of services. The time lapsed between the end time of a station and the start time of the next station was defined as the “wait time.” Figure 3 demonstrates the variability from the IC3 flow by original design and identifies the selected wait times to be the focus of the study. It is important to note that all stations are always completed, despite variation in arrangement. Based on the results, the largest bottlenecks are during chart distribution and the wait to enter the stations. Currently, the clinic uses paper charts that are stored by first letter of last name. The charts are pulled after the patient arrives and lists their name on the appointment roster for the day. • Recommendations to decrease this bottle neck include: • Utilizing the electronic medical record to produce a list of anticipated patients for the following day in order to pull charts ahead of time. • Additional organization of the Chart Room. Currently, 60-80 patients arrive between 7-9am, which creates a bottleneck in the one-way, sequential system due to the bolus of individuals attempting to enter the stations at once. • Recommendations to reduce the time prior to entering the stations include: • Staggering arrivals throughout the morning. Although this option would preclude the ability to conduct one health education talk for the majority of patients prior to the start of clinic. • Splitting the expected patient volume into an AM and PM clinic groups. This option faces the challenge of going against the nationwide culture that clinic is held in the morning only. At the conclusion of this study, the IC3 team was working to implement some of the measures directed at reducing the time to chart distribution and ensuring consistent and adequate staffing. By partnering with a Malawian research colleague, Tafwirapo Chihana, we developed a time-motion protocol that can be implemented repeatedly and simply in the future to evaluate the impact of interventions on patient flow. It is our intention to continue to evaluate the patient flow through IC3 as we increases the patient volume served and the clinical services provided. 1 Wroe, E. B., Kalanga, N., Mailosi, B., Mwalwanda, S., Kachimanga, C., Nyangulu, K., et al. (2015). Leveraging HIV platforms to work toward comprehensive primary care in rural Malawi: The Integrated Chronic Care Clinic. Healthcare (Amsterdam, Netherlands), 3(4), 270-276. 2 Hontelez, J. A., Newell, M. L., Bland, R. M., Munnelly, K., Lessells, R. J., & Barnighausen, T. (2012). Human resources needs for universal access to antiretroviral therapy in South Africa: A time and motion study. Human Resources for Health, 10, 39-4491-10-39. 3 Partners in Health / Abwenzi Pa Za Umoyo, Neno District Council Health Sector. (2015). Integrated Chronic Care Clinic Implementation Guide. Unpublished material. 4 Castelnuovo, B., Babigumira, J., Lamorde, M., Muwanga, A., Kambugu, A., & Colebunders, R. (2009). Improvement of the patient flow in a large urban clinic with high HIV seroprevalence in Kampala, Uganda. International Journal of STD & AIDS, 20(2), 123-124. 5 Partners in Health / Abwenzi Pa Za Umoyo. (2016). Image of Integrated Chronic Care Clinic at Neno District Hospital. Figure 4 demonstrates the minimum, maximum, average and standard deviation regarding the wait time for the selected stations. Chart Ht, Wt, BP, DM, TB & HIV Record s – 3m CO – 4m Nurse – 2m Patient Queue 58m 66m 34m 31m 2h 50m Min: 6m Max: 128m SD: 34m Min: 1m Max: 82m SD: 23m Min: 0m Max: 104m SD: 29m Min: 4m Max: 140m SD: 29m Min: 45m Max: 5h 14m SD: 1h 3m BP & TB DM & HIV Chart Ht, Wt BP & DM TB & HIV Records CO Nurse BP & TB DM, HIV, & Records BP TB, DM & HIV BP & TB DM HIV & Records Patient Queue Patient Queue Chart Ht, Wt BP & DM TB & HIV Records CO Nurse Figure 1 demonstrates the IC3 stations and flow by design. 3 ART NCD Dual Dx Unknown Total Study Patients Total Clinic Volume NDH 55% 35% 4% 6% 107 449 Table 1 displays the patient diagnosis demographics included in the time motion study. Patient Queue Chart Ht, Wt, BP, DM, TB & HIV Records CO Nurse - Organization of Chart Room - Roster of Anticipated Patients Day Prior - Staggered arrival - AM and PM Clinic - Consistent, full staffing - Allow stable NCD patients to complete med refill visit - Additional staff in Pharmacy to allow for more Patient Education by Nurses Figure 2 is a sample data collection slip. Acknowlegments This study would not have been possible without the support and mentorship of the PIH/APZU team, specifically including Tafwirapo Chihana, Katie Cundale, Beth Dunbar, Dr. Chembe Kachimanga, Lila Kerr, Bright Malosi, Dr. Joia Mukherjee, Liberty Neba, Charles Phiri, Dr. Todd Ruderman, Mairead Shaw, Dr. Sara Stulac, Dr. George Talama, and Dr. Emily Wroe. I would also like to thank my mentors and support within the CCGH, including Dr. Amy Blair, Tina Calcagno, Dr. Carrie Cox, Dr. Amy Luke, Dr. Brian Medernach and Lucia Garcia. Figure 5 summarizes the recommendations to improve patient flow through IC3.
