An Information System for Improving Clinical Laboratory Outcomes

Transcription

1 An Information System for Improving Clinical Laboratory Outcomes Adam L. Asare,, Charles W. Caldwell, M.D., Ph.D. Department of Pathology & Anatomical Sciences, Department of Health Management & Informatics, University of Missouri Columbia, School of Medicine, Columbia, Missouri Laboratories performing clinical molecular diagnostic and cytogenetic testing require improved information systems to address their specialized data processing needs. We developed an application that automates result reporting, documents quality assurance information, and tracks specimens. While similar functionality was implemented in both the molecular diagnostic and cytogenetic modules, we present an outcome assessment of the cytogenetic laboratory s use of the system since it maintained a relatively constant number of personnel, test procedures, and samples over a three-year period. Upon implementation, significant reductions occurred in the time taken from receipt of sample to the release of the final report by % (P<.) and 8% (P<.) for peripheral blood and bone marrow samples, respectively. The number of cases processed per technologist increased by % (P=.7). We attribute these gains in quality improvement to the automation of clerical tasks and decision support provided by the information system. INTRODUCTION As the number of clinical genetic tests increase, there is a greater need for laboratory data management applications for cytogenetic and molecular diagnostic testing. Typically, laboratory information systems (LIS) do not address the needs of genetic testing due to their relatively new and specialized nature. Aside from generating reports, clinical data management applications should provide decision support through specimen tracking and the automation of quality assurance and quality control documentation. The health care focus on lowering costs, while maintaining or improving quality, demands that quality measurements be routinely made. However, without automated tools, the measuring of quality may be time-consuming and labor-intensive. Information systems should therefore be designed to automate the measuring and improvement of quality. Monitoring LIS quality improvement based on end-user satisfaction is not ideal. In previous studies, no economy of scale was demonstrated with a system s increased cost through either laboratory staff reduction or increased number of specimens per day, despite perceived end-user satisfaction of the LIS. LIS quality improvement should therefore be evaluated using standard outcome measures. LISs and physician workstations that display laboratory results have demonstrated quality improvement through cost reduction and improved specimen turnaround times (TAT, time taken from receipt of sample to the release of the final report),. There are quality assurance applications for monitoring TAT in clinical chemistry laboratories 7. However, to this author s knowledge, no evaluations have been reported of LISs designed for automated result reporting and performance monitoring tailored to clinical genetic testing. We designed a LIS to improve outcomes in specialty laboratories such as molecular diagnostic, cytogenetics, bone marrow, and flow cytometry at the University of Missouri Ellis Fischel Cancer Center, Columbia, Missouri 8. An evaluation of the cytogenetic component is presented since the cytogenetic laboratory maintained a relatively constant number of personnel, test procedures, and samples over a three-year period. Measures of Outcome METHODS Of the outcome indicators cited as measures of quality laboratory management (Table ), we monitored workload (cases received), number of staff, efficiency, cases processed per technologist (technologist workload), and purchased services 9. Cases received are sub-categorized into those processed within the laboratory (in-house) vs. those sent to reference laboratories (off-site). Off-site analyses are in the purchased services category (Table ). Of the common errors most often produced in the laboratory, we addressed concerns regarding excessive turnaround times (TAT), data entry, patient identification, results not ready when expected, and conflicting results (Table ) 9. The frequency ratio in Table shows that for every analytic error, there are. excessive TAT errors,. data entry errors, patient identification errors, results not ready when expected error, and conflicting results error.

2 Primary Workload Space Derived Efficiency Cases Processed / Technologist Supply Quality Assurance Purchased Services Microbiology media Staff Blood outdating Reagents Table : Indicators of quality laboratory management 9. Items in italics are used to monitor laboratory quality improvement before and after LIS implementation. Type of Error Frequency Ratio* Labeling of specimens. Excessive turnaround times. Data entry. Patient identification Missed orders Results not ready when expected Conflicting results Analytic error *Analytic error rate set at. Table : Common errors in laboratory testing 9. Items in italics are addressed by the LIS. Statistical Analysis A two-tailed t-test with unequal variances was calculated over two -month periods comparing indicators in the Pre-Intervention Period (99-99) to Intervention Period I (99-99) and indicators in the Pre-Intervention Period to Intervention Period II (99-997). February is the start of the year cycle since LIS implementation occurred on February, 99. Application overview The test-ordering interface improves patient identification by displaying prior patient results upon entry of a patient s name, medical record number, or date of birth. Drop-down boxes allow rapid entry of predefined values for diagnosis, physician, patient location, sample type and type of test. Standard values contribute to greater accuracy and speed in processing quality assurance documentation and billing. The case-tracking interface displays pending casework sorted by the number of days a sample has remained within the laboratory. Fields include the specimen s days-in-house, processing status, and assigned technologist. Pending casework can be subsorted by those requiring assignment versus those already assigned to individual technologists. Additional decision support is provided by summary counts appearing at the bottom of the screen. A report generator combines word processing and database functionality by inserting standard statements from a document library. Users update the library as needed. Standard statements in free-text fields, appearing in the report s interpretation or clinical impression, facilitate quality assurance and research endeavors. Our system automates the quality assurance process to address items listed in the College of American Pathologists (CAP) cytogenetic diagnostic proficiency survey. The LIS documents abnormal results and sub-optimal growths for all sample types. Abnormal amniocentesis quality assurance reports include information on follow-up specimens used to confirm the reported abnormality. The LIS also generates TAT reports by specimen type that include the percent of cases meeting CAP TAT guidelines. End-users are able to pinpoint individual cases skewing the TAT averages. Our system models quality assurance systems by not being obtrusive and not taking time away from analyses 7. The LIS is a client-server application using Microsoft SQL Server 7. relational database management system and Visual Basic.. The application s database schema for integrated reporting of molecular diagnostic and flow cytometric results has been previously reported 8. RESULTS In comparing the Pre-Intervention Period to Intervention Period I, the LIS led to a decrease in peripheral blood sample TAT by % (P<.) and a decrease in bone marrow sample TAT by 8% (P<.). Amniotic fluid sample TAT increased by % (P=.9). The number of cases the laboratory processed in-house increased by % (P<.). During the same time period technologist workload (cases processed in-house per technologist) increased by % (P=.7) (Table ). TATs, cases processed in-house, and technologist workload remained relatively constant between Intervention Periods I and II (Table ). Technologist workload reached its highest point during Intervention Period II, with. cases processed per technologist per month despite fluctuations in the number of personnel (Table and Figure, C and C). Factors that may effect TAT and technologist workload outside of the LIS implementation include ) change in number of specimens, ) change in the number of staffing, ) change in technical abilities of personnel and ) restructuring of workflow.

3 Pre-IP (Before use) Blood TAT (avg days/mos).7 Marrow TAT (avg days/mos). Amniotic TAT (avg days/mos) 9. In-house analyses (avg cases/mos). Off-site analyses (avg cases/mos) Tech Workload (cases inhouse/tech/mos) n= months.. IP I ( st year) IP II ( nd year).7.9 P <. P =...7 P <. P < P =.9 P = P <. P < P <. P <... P =.7 P =. Table : Comparison of average monthly TAT by specimen type, cases performed on and off-site, and technologist workload. P-values were computed using a two-tailed t-test with unequal variances over month periods comparing the Pre-Intervention Period (Pre-IP) to Intervention Period I (IP I) and Pre-IP to Intervention Period II (IP II). Change in the number of specimens The number of specimens increased between the Pre-Intervention and Intervention Periods. In the Pre-Intervention Period, the laboratory received 9 cases. In Intervention Period I, 7 cases were received, while in Intervention Period II, 99 cases were received. The increase in the number of specimens could not account for the drop in TAT. Change in number of personnel As with any clinical laboratory, personnel turnover is expected. Our cytogenetic laboratory is considered fully staffed with five technologists. In the Pre-Intervention Period the laboratory operated fully staffed for 9 months and was staffed by three technologists for months (Figure, A and A). In Intervention Period I, the laboratory was fully staffed for months (Figure, B and B). In Intervention Period II, it was staffed by five technologists for 8 months, by four technologists for months, and by three technologists for month (Figure, C and C). Personnel fluctuations in Intervention Period II are similar to that of the Pre- Intervention Period. However, with other variables remaining relatively constant, TATs were significantly reduced in Intervention Period II. Changes in the number of personnel did not contribute to TAT and technologist workload improvements. Change in technical abilities of personnel In the fourth month of the Pre-Intervention Period, two new technologists were hired with little experience in clinical cytogenetics (Figure I, A and A). The poor TAT performance and increase in cases sent out might have been attributable to time spent training during this period. However, in Intervention Period II, three new people were hired with no prior cytogenetic experience, yet TATs remain stable (Figure I, C and C). Change in technical abilities of personnel, alone, could not account for TAT and technologist workload improvements. Restructuring of workflow The LIS led to a restructuring of workflow. In the Pre-Intervention Period, clerical staff not familiar with cytogenetic processes performed billing and report writing functions. During Intervention Periods I and II, laboratory technologists processed billing and final reports. Aside from implementation of the LIS, no new instrumentation, clinical technique or process was introduced into the laboratory. DISCUSSION Most of the TAT and technologist workload improvements can be credited to the LIS casetracking/case-assignment function. In the Pre- Intervention Period, technologists in need of casework selected the top most file from a stack of pending cases that were sorted by days-in-house and importance. This led to ineffective case management since the pending stack was reshuffled through the course of a day. The inability to objectively gauge caseloads in real time led to poor prioritization of casework. This, in turn, led to specimen backlogs and the sending of casework to reference laboratories at increased cost (Table ). Ideally, TATs for peripheral blood and bone marrow samples should be approximately days to remain competitive with reference laboratories. The Pre-Intervention Period shows monthly average TATs that are nearly twice this value (Figure I, A). Using the case-tracking interface, laboratory personnel collectively assigned cases among themselves, taking into consideration their skill level and experience. Weekly assignment sheets helped personnel better prioritize their time, leading to improved average workload from. cases per technologist per month to. in Intervention Period I and. in Intervention Period II. TATs for peripheral blood and bone marrow samples were nearly cut in half using the system (Figure I, B and C).

4 Days A. Pre-Intervention Period (99-99) Days B. Intervention Period I (99-99) Days C. Intervention Period II (99-997) A. Pre-Intervention Period (99-99) vs Number of Technologists B. Intervention Period I (99-99) vs Number of Technologists C. Intervention Period II (99-997) vs Number of Technologists 8 8 Figure : A, B, and C show average monthly in-house turnaround times (TATs) for peripheral blood, bone marrow, and amniotic fluid samples. A, B, and C show the number of cases received per month, distinguishing those kept in-house versus those sent off-site due to workload overflow. A and A show the -month Pre-Intervention Period with increased TATs and a substantial number of cases sent off-site (white bars in A). B, B, C and C show laboratory performance using the LIS, which led to a decrease in TATs, an increase in the number of on-site analyses, and an increase in technologist workload. Fluctuations in laboratory personnel have a minimal effect on laboratory performance. 8

5 The rise in amniotic fluid sample TATs by. days is acceptable considering the increase in inhouse amniotic fluid analyses after the LIS implementation (Table ). In the Pre-Intervention Period, 98 of the amniotic fluid samples received were processed in-house, while in Intervention Period I, 7 of the 99 amniotic fluid samples received were processed in-house. The. day increase is not statistically significant (P=.9) and is compensated by decreased costs through diminished analysis outsourcing. The LIS report generator also helped decrease TATs. Reports were completed within minutes of an analysis as opposed to waiting for the director to review the case and then sending the draft to a secretary for typing. In addition, the report generator reduced report errors and lowered costs through a reduction in personnel. In the Pre- Intervention Period, there was minimal standardization of result reporting as handwritten drafts were given to a secretary for report generation. The manual keyboard input of genetic sex (,XX vs.,xy) was prone to error -- a switch in the last character from X to Y changes a female into a male result. During Intervention Periods I and II, technologists generated report drafts. No genetic sex typographical errors were made using the LIS. A click of a button inserted the karyotype, interpretation, and clinical impression into the report. This was followed by system verification of the phenotypic sex with the reported genotypic sex. In terms of reducing costs, management did not re-hire a clerical. full-time employee (FTE) dedicated to cytogenetic report generation. The LIS was more efficient and accurate in producing reports. The LIS provides quality improvement by allowing end-users to anticipate and prevent problems before they occur. There is high end-user satisfaction with the application as it enables personnel to play a more active role in case management and workload decisions. We have not had to resort to management practices of emphasizing quality through inspection and finding fault, particularly with individuals, after errors have occurred. The LIS has been instrumental in alerting end-users of potential problems preemptively, which in turn has improved personnel morale. In conclusion, we have shown that a LIS providing decision support and automated data processing for cytogenetic testing offers significant quality improvement by reducing specimen TAT and increasing technologist workload. Most LISs do not include modules for cytogenetic or molecular diagnostic testing. Therefore, there is a need to create specialized applications to improve the reporting and quality assurance process for laboratories performing clinical genetic testing. Acknowledgments This work has been supported in part by National Library of Medicine grant, LM References. Holtzman NA, Watson MS. Promoting Safe and Effective Genetic Testing in the United States: Final Report of the Task Force on Genetic Testing, 997 Sep.. Lundy MS, Hammond WE, Lobach DF. Documenting Data Delivery: Design, Deployment, and Decision. Proceedings of the 99 AMIA Fall Symposium, 99 Oct -, Washington, DC. p Bates DW, Pappius E, Kuperman GJ, et al. Using Information Systems to Measure and Improve Quality. International Journal of Medical Informatics 999;:-.. Elevitch F, Treling C, Spackman K, et al. A clinical laboratory information systems survey: A challenge for the decade. Arch of Pathol Lab Med 99;7():-.. Pelegri MD, Garcia-Beltran L, Pascual C. Improvement of emergency and routine turnaround time by data processing and instrumentation changes. Clinica Chimica Acta 99;8():-7.. Connelly DP, Sielaff BH, Willard KE. A clinician s workstation for improving laboratory use: Integrated display of laboratory results. Am J Clin Pathol 99;:-. 7. Rollo JL, Fauser BA. Computers in total quality management. Arch Pathol Lab Med 99;7: Asare AL, Huda H, Klimczak JC, Caldwell CW. Integrating molecular diagnostic and flow cytometric reporting for improved longitudinal monitoring of HIV patients. Proceedings of the 998 AMIA Fall Symposium, 998 Nov 7-, Orlando, FL. p Koepke JA, Klee GC. The Process of Quality Assurance. Arch Pathol Lab Med 99;- 9.. Bartlett, RC. Trends in Quality Management [published erratum appears in Arch Pathol Lab Med 99 Mar;():9]. Arch Pathol Lab Med 99;-.. Berwick DM. Continuous Improvement as an Ideal in Health Care. JAMA 989;:-.