Reducing ED Stay by Providing Laboratory Results on Time
Reducing ED Stay by Providing Laboratory Results on Time
For laboratory turnaround time computation, we used the time from when a sample was received in the laboratory to the time when results were verified and reported out. We then computed the mean turnaround time and TAT-OP on a monthly basis from July 2003, when we began our process improvement project, through March 2012, 6 months after achieving a goal of Six Sigma–level performance. Specifically, during this period, we monitored the turnaround time for potassium, an important analyte in the basic and comprehensive metabolic panel for the ED, noting the raw count of tests not meeting the promised turnaround time targets. We also monitored turnaround time targets for hematology for our ED services. A turnaround time target mean of 40 minutes was selected by our ED physicians as desirable for potassium in chemistry, with 60 minutes considered an acceptable outlier limit, while 30 minutes was set as the target mean for hemoglobin in hematology. To achieve these specific turnaround time targets, we initiated monthly monitors and monitoring dashboards as part of an active program employing modern management principles of Lean and Six Sigma. To ensure success, we designed this quality improvement program to include all essential steps of the laboratory process, including preanalytical, analytical, and postanalytical. Clearly, the focus of this article is chemistry improvements through deployment of TLA and POC testing. However, to date, we have also achieved a 3.1 σ level in our hematology ED turnaround time outcomes data using the aggressive 30-minute benchmark for hemoglobin outliers described earlier. More than 93% of hemoglobin results have met the turnaround time target mean set for hematology. Note that hemoglobin is part of the complete blood count (CBC), and thus the hemoglobin turnaround time reflects the completed CBC panel turnaround time. The approach we used in hematology was similar to our chemistry automation; we employed automation tracks, Remisol Advance middleware (Beckman Coulter, Brea, CA) for autoverification, and a 24/7 dashboard screen to identify problems in real time. For the preanalytical phase, we implemented electronic physician order management on our MEDITECH hospital information system (Medical Information Technology, Westwood, MA) along with local barcode printing using MedPlus (Medical Information Technology) and JMI Barcodes (JMI Barcodes, Raleigh, NC). We deployed Pevco pneumatic tubes (Pevco, Baltimore, MD) throughout the hospitals and clinics for specimen delivery to the central specimen receiving and processing (CRP) area. We also initiated a real-time specimen collection program using nurses, thus eliminating the phlebotomy team. Using Lean principles, our CRP area was revamped so that specimens are handled only once.
For our decentralized critical care testing initiative, we deployed POC handheld testing devices and managed this expanded program from the core laboratory with 2 registered medical technologists serving as POC coordinators. Interfaces for these remote POC devices were handled by local network hubs with each device given a unique IP address. A 7-bed chest pain unit previously had been established in the ED to support bedside cardiac marker testing on a POC testing device. Middleware was deployed to manage all POC testing (RALS; Medical Automation Systems, Charlottesville, VA), including autoverification and quality control review by core laboratory technologists.
For the analytical and postanalytical testing processes in the core laboratory, a total automation robotic track system (Beckman Coulter) was installed in the chemistry section in 2003–2004. It included (1) a specimen scanning and tracking/software Preplink interface; (2) a rapid in-line robotic centrifuge with a 4-minute spin cycle; (3) a conveyor puck system for a physical track-instrument interface connection for our 3 chemistry analyzers, SYNCHRON LX20 (Beckman Coulter), and our Advia Centaur immunology systems (Siemens, Tarrytown, NY); (4) specimen tube decappers and recappers; and (5) an in-line robotic refrigerated specimen "stockyard" for up to 3,000 tubes. The implementation plan for the system included project management tools such as Gantt charts and a full-time project engineer for our site; we held biweekly progress meetings and required strict adherence to assignments, testing, validation, and timelines. The entire chemistry system was implemented over a 5-month time frame and completed in April 2004, 1.5 months ahead of the projected completion date.
Middleware software, Remisol Advance (Beckman Coulter), was obtained. This middleware manages the following analytical and postanalytical functions: (1) track automation; (2) instrument interfaces and monitoring; (3) autoverification and release of laboratory results; (4) specimen retrieval; (5) quality control, including real-time quality control based on exponentially weighted patient moving averages; (6) instrument interfacing and communication with the MEDITECH laboratory and hospital information systems; and (7) a 5-screen dashboard to monitor the entire system in real time. The 5-screen dashboard deployed in chemistry also includes a real-time monitor of pending ED test requests. One core laboratory technologist operates the dashboard workstation on a 24/7 basis.
Autoverification rules were used on the Remisol Advance middleware with algorithms to check all patient results for problems based on (1) normal range checks; (2) critical range checks; (3) absurd result flags; (4) specimen indices for hemolysis, lipemia, and icterus; (5) instrument flags, including photometer range checks; (6) computational checks, including anion gaps; (7) delta checks; (8) quality control range checks; and (9) patient running means checks. When patients' results fail autoverification for any rule, the dashboard immediately alerts the technologist and, depending on the rule involved, also lists corrective action consistent with approved laboratory policy for handling the problem identified.
For our paperless compliance initiative, we implemented Compliance360 software applications (Sai Global, Alpharetta, GA). Using this application, we eliminated all paper records, including laboratory procedures and policies, documentation for inspections and checklists, method quality control data, and evaluation studies. We also implemented Compliance360 applications for e-mail support for timely and online review of documents by laboratory supervisors and directors.
All Six Sigma calculations were performed on an Excel worksheet (Microsoft Office 2003 and 2007; Microsoft, Redmond, WA) programmed with macros and graphics features designed specifically for this purpose (QI Macros SPC Software for Excel developed by Jay Arthur; http://www.spcforexcel.com/statistical-tools-and-features-included-spc-for-excel). Calculations of σ levels were based on a proprietary best-fit relationship between industry-accepted defects and σ levels. Prior to importing to this spreadsheet, raw data were extracted from our MEDITECH laboratory information system database and imported into an Access database (Microsoft Office 2003 and 2007). Ad hoc real-time queries were made in the Access database to specifically select subsets of data for import into the Excel Six Sigma spreadsheet.
To estimate ROI for the laboratory, we calculated the paid hours per test. For this calculation, the total paid monthly hours for the technologists working in chemistry was divided by the total number of reportable tests generated over that month's pay period.
Materials and Methods
For laboratory turnaround time computation, we used the time from when a sample was received in the laboratory to the time when results were verified and reported out. We then computed the mean turnaround time and TAT-OP on a monthly basis from July 2003, when we began our process improvement project, through March 2012, 6 months after achieving a goal of Six Sigma–level performance. Specifically, during this period, we monitored the turnaround time for potassium, an important analyte in the basic and comprehensive metabolic panel for the ED, noting the raw count of tests not meeting the promised turnaround time targets. We also monitored turnaround time targets for hematology for our ED services. A turnaround time target mean of 40 minutes was selected by our ED physicians as desirable for potassium in chemistry, with 60 minutes considered an acceptable outlier limit, while 30 minutes was set as the target mean for hemoglobin in hematology. To achieve these specific turnaround time targets, we initiated monthly monitors and monitoring dashboards as part of an active program employing modern management principles of Lean and Six Sigma. To ensure success, we designed this quality improvement program to include all essential steps of the laboratory process, including preanalytical, analytical, and postanalytical. Clearly, the focus of this article is chemistry improvements through deployment of TLA and POC testing. However, to date, we have also achieved a 3.1 σ level in our hematology ED turnaround time outcomes data using the aggressive 30-minute benchmark for hemoglobin outliers described earlier. More than 93% of hemoglobin results have met the turnaround time target mean set for hematology. Note that hemoglobin is part of the complete blood count (CBC), and thus the hemoglobin turnaround time reflects the completed CBC panel turnaround time. The approach we used in hematology was similar to our chemistry automation; we employed automation tracks, Remisol Advance middleware (Beckman Coulter, Brea, CA) for autoverification, and a 24/7 dashboard screen to identify problems in real time. For the preanalytical phase, we implemented electronic physician order management on our MEDITECH hospital information system (Medical Information Technology, Westwood, MA) along with local barcode printing using MedPlus (Medical Information Technology) and JMI Barcodes (JMI Barcodes, Raleigh, NC). We deployed Pevco pneumatic tubes (Pevco, Baltimore, MD) throughout the hospitals and clinics for specimen delivery to the central specimen receiving and processing (CRP) area. We also initiated a real-time specimen collection program using nurses, thus eliminating the phlebotomy team. Using Lean principles, our CRP area was revamped so that specimens are handled only once.
For our decentralized critical care testing initiative, we deployed POC handheld testing devices and managed this expanded program from the core laboratory with 2 registered medical technologists serving as POC coordinators. Interfaces for these remote POC devices were handled by local network hubs with each device given a unique IP address. A 7-bed chest pain unit previously had been established in the ED to support bedside cardiac marker testing on a POC testing device. Middleware was deployed to manage all POC testing (RALS; Medical Automation Systems, Charlottesville, VA), including autoverification and quality control review by core laboratory technologists.
For the analytical and postanalytical testing processes in the core laboratory, a total automation robotic track system (Beckman Coulter) was installed in the chemistry section in 2003–2004. It included (1) a specimen scanning and tracking/software Preplink interface; (2) a rapid in-line robotic centrifuge with a 4-minute spin cycle; (3) a conveyor puck system for a physical track-instrument interface connection for our 3 chemistry analyzers, SYNCHRON LX20 (Beckman Coulter), and our Advia Centaur immunology systems (Siemens, Tarrytown, NY); (4) specimen tube decappers and recappers; and (5) an in-line robotic refrigerated specimen "stockyard" for up to 3,000 tubes. The implementation plan for the system included project management tools such as Gantt charts and a full-time project engineer for our site; we held biweekly progress meetings and required strict adherence to assignments, testing, validation, and timelines. The entire chemistry system was implemented over a 5-month time frame and completed in April 2004, 1.5 months ahead of the projected completion date.
Middleware software, Remisol Advance (Beckman Coulter), was obtained. This middleware manages the following analytical and postanalytical functions: (1) track automation; (2) instrument interfaces and monitoring; (3) autoverification and release of laboratory results; (4) specimen retrieval; (5) quality control, including real-time quality control based on exponentially weighted patient moving averages; (6) instrument interfacing and communication with the MEDITECH laboratory and hospital information systems; and (7) a 5-screen dashboard to monitor the entire system in real time. The 5-screen dashboard deployed in chemistry also includes a real-time monitor of pending ED test requests. One core laboratory technologist operates the dashboard workstation on a 24/7 basis.
Autoverification rules were used on the Remisol Advance middleware with algorithms to check all patient results for problems based on (1) normal range checks; (2) critical range checks; (3) absurd result flags; (4) specimen indices for hemolysis, lipemia, and icterus; (5) instrument flags, including photometer range checks; (6) computational checks, including anion gaps; (7) delta checks; (8) quality control range checks; and (9) patient running means checks. When patients' results fail autoverification for any rule, the dashboard immediately alerts the technologist and, depending on the rule involved, also lists corrective action consistent with approved laboratory policy for handling the problem identified.
For our paperless compliance initiative, we implemented Compliance360 software applications (Sai Global, Alpharetta, GA). Using this application, we eliminated all paper records, including laboratory procedures and policies, documentation for inspections and checklists, method quality control data, and evaluation studies. We also implemented Compliance360 applications for e-mail support for timely and online review of documents by laboratory supervisors and directors.
All Six Sigma calculations were performed on an Excel worksheet (Microsoft Office 2003 and 2007; Microsoft, Redmond, WA) programmed with macros and graphics features designed specifically for this purpose (QI Macros SPC Software for Excel developed by Jay Arthur; http://www.spcforexcel.com/statistical-tools-and-features-included-spc-for-excel). Calculations of σ levels were based on a proprietary best-fit relationship between industry-accepted defects and σ levels. Prior to importing to this spreadsheet, raw data were extracted from our MEDITECH laboratory information system database and imported into an Access database (Microsoft Office 2003 and 2007). Ad hoc real-time queries were made in the Access database to specifically select subsets of data for import into the Excel Six Sigma spreadsheet.
To estimate ROI for the laboratory, we calculated the paid hours per test. For this calculation, the total paid monthly hours for the technologists working in chemistry was divided by the total number of reportable tests generated over that month's pay period.
