AFJOG

ORIGINAL RESEARCH theatre time, followed by lack of blood products (9%, n = 8), lack of high care or ICU beds (8%, n = 8) and inadequate patient preparation (6%, n=6). Staff shortages (3%, n = 3) contributed to the least proportion of avoidable cancellations. Patient factors were the next common category of avoidable factors contributing to a total of 15 (14%) cancellations. In this category, delay in seeking medical care (11%, n = 11) was more common than the performance of unsafe abortions (4%, n = 4). The next most prevalent category was medical care (8%, n = 8). Under this category, the most common factor was an error in diagnosis (5%, n = 5). Problems of inappropriate care (n = 1) and inadequate patient monitoring (n = 1) were each contributing under 1%. As shown in Table 4, care at inappropriate facility levels contributed to 4% (n = 4) of avoidable factors. This was due to palliative care being provided in a secondary or tertiary care hospital. Delays in diagnosis also contributed to 4% (n = 4) of the avoidable factors. In this category, omission of a screening or diagnostic test (3%, n = 3) was a more common cause of delay in diagnosis when compared to actual delay to a tertiary level care facility (1%, n = 1). Table 4: Avoidable factors (n = 106) Description n (%) Administrative factors (75) Lack of theatre time 49 (46.2) Lack of blood products 9 (8.5) Lack of HCA or ICU bed 8 (7.5) Inadequate patient preparation (not starved, no results) 6 (5.7) Staff shortages 3 (2.8) Patient factors (n = 15) Delay in seeking medical care 11 (10.4) Unsafe abortion 4 (3.7) Medical care (n = 8) Diagnosis 6 (5.7) Inappropriate level 1 (0.9) Monitoring problem 1 (0.9) Inappropriate facility (n = 4) Palliative care in a secondary or tertiary hospital 4 (3.7) Delay in diagnosis (n = 4) Screening/diagnostic test not performed. 3 (2.8) Delay in referral to a tertiary institute 1 (0.9) Comparison by hospital of admission Table 5 shows the comparisons of hospital admission between SBAH and KPTH. Only the admission unit and type of admission were significantly different (p <0.05). However, the other characteristics, including age, parity, gravidity and HIV-positive status, were not statistically different between the two hospitals. Table 5: Comparison of key findings by hospital SBAH KPTH Description Median (IQR) or n (%) Median (IQR) or n (%) P-value Age years (n = 217) 44 (34 – 57) 42 (34 – 54) 0.34 Unit of admission (n = 217) 0.001 Oncology 82 (68.3) 52 (53.6) General gynaecology 9 (7.5) 45 (46.4) Uro-gynaecology 20 (16.7) 0 (0) Reproductive gynaecology 8 (6.7) 0 (0) Obstetrics 1 (0.0) 0 (0) Type of admission (n = 217) 0.001 Elective 58 (48.3) 60 (62.4) Oncology 39 (32.5) 18 (18.5) Emergency 23 (19.2) 19 (19.25) Treatment intention (n = 217) 0.12 Surgical 92 (76.7) 84 (86.6) Medical 17 (14.2) 7 (7.2) Palliative 11 (9.1) 6 (6.2) Parity (n = 217) 2 (1 – 3) 2 (2 – 3) 0.43 Gravidity (n = 217) 2 (1 – 3) 3 (2 – 4) 0.11 HIV positive (n = 80) 43 (35.8) 37 (38.1) 0.92 *P-value obtained from Wilcoxon Rank-Sum, Chi-Square or Fischer’s Exact test DISCUSSION This study reported the overall incidence rate and the nature of critical incidents among gynaecological hospital admissions at Steve Biko Academic and Kalafong Provincial Tertiary Hospitals in Gauteng Province of South Africa. The overall incidence rate of critical incidents among gynaecological patients over 12 months was 6.7%. The incidence rates were not significantly different between the two hospitals, African Journal of Obstetrics and Gynaecology | Volume 1 | Issue 1 | 2023 | 19