Understanding the barriers to setting up a healthcare quality improvement process in resource-limited settings: a situational analysis at the Medical Department of Kamuzu Central Hospital in Lilongwe, Malawi.

BACKGROUND: Knowledge regarding the best approaches to improving the quality of healthcare and their implementation is lacking in many resource-limited settings. The Medical Department of Kamuzu Central Hospital in Malawi set out to improve the quality of care provided to its patients and establish itself as a recognized centre in teaching, operations research and supervision of district hospitals. Efforts in the past to achieve these objectives were short-lived, and largely unsuccessful. Against this background, a situational analysis was performed to aid the Medical Department to define and prioritize its quality improvement activities. METHODS: A mix of quantitative and qualitative methods was applied using checklists for observed practice, review of registers, key informant interviews and structured patient interviews. The mixed methods comprised triangulation by including the perspectives of the clients, healthcare providers from within and outside the department, and the field researcher's perspectives by means of document review and participatory observation. RESULTS: Human resource shortages, staff attitudes and shortage of equipment were identified as major constraints to patient care, and the running of the Medical Department. Processes, including documentation in registers and files and communication within and across cadres of staff were also found to be insufficient and thus undermining the effort of staff and management in establishing a sustained high quality culture. Depending on their past experience and knowledge, the stakeholder interviewees revealed different perspectives and expectations of quality healthcare and the intended quality improvement process. CONCLUSIONS: Establishing a quality improvement process in resource-limited settings is an enormous task, considering the host of challenges that these facilities face. The steps towards changing the status quo for improved quality care require critical self-assessment, the willingness to change as well as determined commitment and contributions from clients, staff and management.

Informatics solutions for bridging the gap between clinical and laboratory services in a low-resource setting.

Background: There has been little formal analysis of laboratory systems in resource-limited settings, despite widespread consensus around the importance of a strong laboratory infrastructure. Objectives: This study details the informational challenges faced by the laboratory at Kamuzu Central Hospital, a tertiary health facility in Malawi; and proposes ways in which informatics can bolster the efficiency and role of low-resource laboratory systems. Methods: We evaluated previously-collected data on three different aspects of laboratory use. A four-week quality audit of laboratory test orders quantified challenges associated with collecting viable specimens for testing. Data on tests run by the laboratory over a one-year period described the magnitude of the demand for laboratory services. Descriptive information about the laboratory workflow identified informational process breakdowns in the pre-analytical and post-analytical phases and was paired with a 24-hour sample of laboratory data on results reporting. Results: The laboratory conducted 242 242 tests over a 12-month period. The four-week quality audit identified 54% of samples as untestable. Prohibitive paperwork errors were identified in 16% of samples. Laboratory service workflows indicated a potential process breakdown in sample transport and results reporting resulting from the lack of assignment of these tasks to any specific employee cadre. The study of result reporting time showed a mean of almost six hours, with significant variation. Conclusions: This analysis identified challenges in each phase of laboratory testing. Informatics could improve the management of this information by streamlining test ordering and the communication of test orders to the laboratory and results back to the ordering physician.

Modeling return on investment for an electronic medical record system in Lilongwe, Malawi.

OBJECTIVE: To model the financial effects of implementing a hospital-wide electronic medical record (EMR) system in a tertiary facility in Malawi. MATERIALS AND METHODS: We evaluated three areas of impact: length of stay, transcription time, and laboratory use. We collected data on expenditures in these categories under the paper-based (pre-EMR) system, and then estimated reductions in each category based on findings from EMR systems in the USA and backed by ambulatory data from low-income settings. We compared these potential savings accrued over a period of 5 years with the costs of implementing the touchscreen point-of-care EMR system at that site. RESULTS: Estimated cost savings in length of stay, transcription time, and laboratory use totaled US$284 395 annually. When compared with the costs of installing and sustaining the EMR system, there is a net financial gain by the third year of operation. Over 5 years the estimated net benefit was US$613 681. DISCUSSION: Despite considering only three categories of savings, this analysis demonstrates the potential financial benefits of EMR systems in low-income settings. The results are robust to higher discount rates, and a net benefit is realized even under more conservative assumptions. CONCLUSIONS: This model demonstrates that financial benefits could be realized with an EMR system in a low-income setting. Further studies will examine these and other categories in greater detail, study the financial effects at different levels of organization, and benefit from post-implementation data. This model will be further improved by substituting its assumptions for evidence as we conduct more detailed studies.

Early experience after developing a pathology laboratory in Malawi, with emphasis on cancer diagnoses.

BACKGROUND: Despite increasing cancer burden in Malawi, pathology services are limited. We describe operations during the first 20 months of a new pathology laboratory in Lilongwe, with emphasis on cancer diagnoses. METHODS AND FINDINGS: We performed a cross-sectional study of specimens from the Kamuzu Central Hospital pathology laboratory between July 1, 2011 and February 28, 2013. Patient and specimen characteristics, and final diagnoses are summarized. Diagnoses were categorized as malignant, premalignant, infectious, other pathology, normal or benign, or nondiagnostic. Patient characteristics associated with premalignancy and malignancy were assessed using logistic regression. Of 2772 specimens, 2758 (99%) with a recorded final diagnosis were included, drawn from 2639 unique patients. Mean age was 38 years and 63% were female. Of those with documented HIV status, 51% had unknown status, and 36% with known status were infected. Histologic specimens comprised 91% of cases, and cytologic specimens 9%. Malignant diagnoses were most common overall (n = 861, 31%). Among cancers, cervical cancer was most common (n = 117, 14%), followed by lymphoma (n = 91, 11%), esophageal cancer (n = 86, 10%), sarcoma excluding Kaposi sarcoma (n = 75, 9%), and breast cancer (n = 61, 7%). HIV status was known for 95 (11%) of malignancies, with HIV prevalence ranging from 9% for breast cancer to 81% for cervical cancer. Increasing age was consistently associated with malignancy [bivariable odds ratio 1.24 per decade increase (95% CI 1.19-1.29) among 2685 patients with known age; multivariable odds ratio 1.33 per decade increase (95% CI 1.14-1.56) among 317 patients with known age, gender, and HIV status], while HIV infection and gender were not. CONCLUSIONS: Despite selection and referral bias inherent in these data, a new pathology laboratory in Lilongwe has created a robust platform for cancer care and research. Strategies to effectively capture clinical information for pathologically confirmed cancers can allow these data to complement population-based registration.