Analysis of STAT Laboratory Turnaround Times Before and After Conversion of the Hospital Information System

Abstract

BACKGROUND: Modern electronic health record systems (EHRS) reportedly offer advantages including improved quality, error prevention, cost reduction, and increased efficiency. This project reviewed the impact on specimen turnaround times (TAT) and percent compliance for specimens processed in a STAT laboratory after implementation of an upgraded EHRS. METHODS: Before EHRS implementation, laboratory personnel received instruction and training for specimen processing. One laboratory member per shift received additional training. TAT and percent compliance data sampling occurred 4 times monthly for 13 months post-conversion and were compared with the mean of data collected for 3 months pre-conversion. Percent compliance was gauged using a benchmark of reporting 95% of all specimens within 7 min from receipt. RESULTS: Control charts were constructed for TAT and percent compliance with control limits set at 2 SD and applied continuously through the data collection period. TAT recovered to pre-conversion levels by the 6th month post-conversion. Percent compliance consistently returned to pre-conversion levels by the 10th month post-conversion. Statistical analyses revealed the TAT were significantly longer for 3 months post-conversion (P < .001) compared with pre-conversion levels. Statistical significance was not observed for subsequent groups. Percent compliance results were significantly lower for 6 months post-conversion (P < .001). Statistical significance was not observed for subsequent groups. CONCLUSIONS: Extensive efforts were made to train and prepare personnel for challenges expected after the EHRS upgrade. Specific causes identified with the upgraded EHRS included multiple issues involving personnel and the EHRS. These data suggest that system and user issues contributed to delays in returning to pre-conversion TAT and percent compliance levels following the upgrade in the EHRS.

Identification of the essential components of quality in the data collection process for public health information systems

“…14,35,37,47 Designated unit or individuals to monitor data quality and prevent data collection mismanagement are recommended. 44,47,49,53,55 A veteran health worker register could remind data collectors to correct inaccurate data items, 44,62 and provide additional training, supervision, and incentives. 31 Holding regular meetings with medical or clinical staff and a data registrar is useful to address missing or inconsistent data.…”

“…However, changes in the project procedures and system configuration over time may lead to a decline in data quality if deployed against established guidelines or specifications on data collection for PHIS. 11,53…”

“…Attributes of good leadership include (1) strengthened coordination, cooperation, and communication among government agencies and between healthcare facilities and health professionals; (2) recognition of the importance of data to be collected; (3) provision of sufficient funding; and (4) allocation of full-time staff or specific staff to data collection. 35,44,51–53,69 Examples of good leadership include the development of a less resource-intensive approach using strategies of decentralization to empower the management team in the field and establishing a multi-level supervision network that includes health departments and healthcare facilities. 51,52 Supervisors can perform real-time field quality assurance and control activities.…”

“…However, changes in the project procedures and system configuration over time may lead to a decline in data quality if deployed against established guidelines or specifications on data collection for PHIS. 11,53 Integration of different systems is important in the PHIS. Multiple systems and files may impair the quality of the data collection process if data are from the various sources.…”

“…35,57,70,71 Information system An information system is a combination of hardware, software, infrastructure, and trained personnel. 5 Of the 45 articles, 30 assessed the quality of information systems 14,28,[33][34][35][36]38,[40][41][42][43][44][45]47,48,[51][52][53]55,57,58,[60][61][62][64][65][66][67]69,70 (see Table 1). A regular system of data quality checks may be more cost effective and reliable to ensure data quality.…”