How to Upload Glucose Meter to Iu Health
JMIR Med Inform. 2019 Jan-Mar; 7(1): e11873.
Transcription Errors of Claret Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Assay Toward Electronic Medical Tape-Glucometer Interoperability
Monitoring Editor: Christian Lovis
Azizeh Khaled Sowan, RN, MSDA, PhD, 
1 Ana Vera, RN, MSN,2 Ashwin Malshe, PhD,3 and Charles Reed, RN, CNRN, PhD2
i Schoolhouse of Nursing, University of Texas Health at San Antonio, San Antonio, TX, United States
ii Center for Clinical Excellence, Academy Health System, San Antonio, TX, United States
3 Higher of Business, University of Texas at San Antonio, San Antonio, TX, The states
Azizeh Khaled Sowan, School of Nursing, University of Texas Health at San Antonio, 7703 Floyd Curl Bulldoze - MC 7975, San Antonio, TX, 78229, United States, Telephone: 1 210 567 5799, Fax: 1 210 567 1719, Email: ude.ascshtu@nawos.
Azizeh Khaled Sowan
1 School of Nursing, Academy of Texas Health at San Antonio, San Antonio, TX, Usa
Ana Vera
ii Center for Clinical Excellence, University Health System, San Antonio, TX, Us
Ashwin Malshe
3 Higher of Business organisation, University of Texas at San Antonio, San Antonio, TX, United States
Charles Reed
2 Center for Clinical Excellence, University Health Arrangement, San Antonio, TX, United States
Received 2018 Aug 8; Revisions requested 2018 Dec 27; Revised 2019 Jan ten; Accepted 2019 January 11.
Abstract
Groundwork
Critically ill patients crave constant point-of-care blood glucose testing to guide insulin-related decisions. Transcribing these values from glucometers into a newspaper log and the electronic medical tape is very common yet error-prone in intensive care units, given the lack of connectivity between glucometers and the electronic medical tape in many United states of america hospitals.
Objective
We examined (i) transcription errors of glucometer blood glucose values documented in the paper log and in the electronic medical tape vital signs flow sheet in a surgical trauma intensive care unit, (2) insulin errors resulting from transcription errors, (3) lack of documenting these values in the paper log and the electronic medical record vital signs flow canvas, and (4) average fourth dimension for docking the glucometer.
Methods
This secondary information analysis examined 5049 point-of-care claret glucose tests. We obtained values of blood glucose tests from bidirectional interface software that transfers the meters' data to the electronic medical tape, the paper log, and the vital signs period sheet. Nosotros obtained patient demographic and clinical-related information from the electronic medical record.
Results
Of the 5049 claret glucose tests, which were pertinent to 234 patients, the total numbers of undocumented or untranscribed tests were 608 (12.04%) in the paper log, 2064 (40.88%) in the flow sheet, and 239 (iv.73%) in both. The numbers of transcription errors for the documented tests were 98 (2.21% of 4441 documented tests) in the paper log, 242 (8.eleven% of 2985 tests) in the flow canvas, and 43 (1.64% of 2616 tests) in both. The numbers of transcription errors per patient were 0.4 (98 errors/234 patients) in the newspaper log, 1 (242 errors/234 patients) in the flow sheet, and 0.2 in both (43 errors/234 patients). Transcription errors in the paper log, the flow sheet, and in both resulted in 8, 24, and ii insulin errors, respectively. As a issue, patients were given a lower or higher insulin dose than the dose they should take received had in that location been no errors. Discrepancies in insulin doses were two to eight U lower doses in paper log transcription errors, 10 U lower to 3 U higher doses in catamenia canvass transcription errors, and 2 U lower in transcription errors in both. Overall, 30 unique insulin errors afflicted 25 of 234 patients (10.7%). The average time from point-of-care testing to meter docking was 8 hours (median v.five hours), with some taking 56 hours (two.three days) to be uploaded.
Conclusions
Given the high dependence on glucometers for point-of-care claret glucose testing in intensive care units, total electronic medical record-glucometer interoperability is required for consummate, accurate, and timely documentation of blood glucose values and elimination of transcription errors and the subsequent insulin-related errors in intensive care units.
Keywords: transcription errors, claret glucose, insulin errors, interoperability, glucometer, electronic medical records, secondary data analysis, intensive care units, medication errors
Introduction
Background
Glycemic control in critically ill patients is essential to ameliorate clinical outcomes and decrease morbidity and bloodshed [1-8], specifically for patients admitted to intensive care units (ICUs) for more than 3 days [2] and for patients admitted to surgical trauma ICUs (STICUs) compared with medical ICUs [7]. Critically ill patients require abiding point-of-care tests (POCTs) for blood glucose to guide initiation and titration decisions regarding continuous insulin infusion following insulin management protocols. Handheld claret glucose monitoring devices or glucometers are widely used in ICUs for this purpose for convenience and portability [nine,10].
Transcribing blood glucose readings from glucometers into a paper log and different menses sheets in the electronic medical tape (EMR) by health intendance professionals is a very common yet mistake-decumbent exercise in ICUs, given the lack of interoperability or connectivity between glucometers and the EMR in many United states hospitals [11]. Interoperability allows for wireless transfer of claret glucose values from glucometers to the EMR without the demand for manual information entry. Despite the phone call for system interoperability and emerging research describing frameworks and prototypes for seamless integration of medical device data into the EMR using unlike connectivity standards [12-15], medical device-EMR connectivity is express in the United States. In a national survey of 825 The states hospitals, the Wellness Information and Direction Systems Society Analytics team reported a lack of any interface between EMRs and medical devices in 70% of the hospitals. The remaining 30% of the hospitals reported an interface of an average of 2.half dozen device types (out of 11 devices) with their EMRs. Interestingly, none of the hospitals provided an interface between glucometers and the EMR [11].
Extensive literature exists on the use of glucometers in ICUs. However, most studies focused on the glucometers' accuracy in comparison with other blood glucose analytical measures [sixteen-24]. Research on transcription errors is also available [25-27]; withal, there is a paucity of inquiry on transcription errors of blood glucose values obtained past glucometers into the EMRs and the subsequent insulin errors [28]. Although the apply of glucometers with high specificity and sensitivity is essential in critical care settings to foreclose harmful effects of erroneous claret glucose readings and the subsequent underdose or overdose of insulin therapy, accurate and instant documentation of blood glucose values obtained by glucometers into the EMR is equally important to inform glycemic control and insulin management decisions.
Objective
This study examined (one) transcription errors of blood glucose values obtained by a glucometer that were documented in the paper log by technicians and in the EMR vital signs flow sheet by nurses in the ICU, (2) insulin errors resulting from transcription errors of blood glucose values, (three) lack of documenting blood glucose values in the paper log and the EMR vital signs catamenia sheet, and (4) meter docking time.
Methods
Design, Sample, and Setting
This secondary information analysis study examined 5049 claret glucose tests for transcription errors, insulin errors, lack of documenting blood glucose values in the paper log and the EMR, and meter docking time. The study took place in a 30-bed STICU located in a 705-bed academy teaching hospital with a large referral base in the southwestern United states. The STICU has an almanac admission rate of 1600 patients and an approximate monthly access charge per unit of 133 patients. At the time of the study, there were 46 full-fourth dimension and xi part-time nurses and thirteen technicians working in the unit. The average range of blood glucose POCTs performed on patients in the unit is 4200 to 4300 tests per month.
After obtaining institutional review board blessing from the Academy of Texas and the University Wellness System (number 20140330H), nosotros performed the audit of blood glucose tests and insulin information in a twenty% stratified sample of all blood glucose tests available in the meters for patients admitted during 4 months (July to October 2016). Stratification was based on the working shift (day or nighttime) equally the only possible cistron that may innovate transcription errors of blood glucose readings as a result of fatigue expected at the terminate of each working shift and on the night shift. Additionally, when we selected a claret glucose test, we too included all claret glucose tests pertinent to the same patient within the same episode of access to evaluate errors per patient. This resulted in a total of 5049 claret glucose tests.
Description of the Point-of-Care Testing of Blood Glucose
The point-of-care glucose testing device is Accu-Chek Inform Two (Roche Diagnostics Corporation, Indianapolis, IN, United states of america). Effigy ane depicts a functional workflow model for this process of testing. The process starts by the physician ordering a POCT. The nurse informs the technician nigh the order, who in plough performs the test using the glucometer and transcribes the effect into a newspaper log—a grid that includes the patient's proper name, visit identification number (VIN), room number, fourth dimension and appointment of the exam, and the result. The VIN is a unique number for each patient episode of admission that is obtained past scanning the patient'south wristband at the time of performing the test.
Workflow model of the indicate-of-care-testing of blood glucose. PC: personal reckoner.
Nurses then manually enter the readings for each patient into the EMR vital signs flow sheet and apply this information to inform their insulin management decisions following physician orders and insulin management protocols. Clinical decisions include whether to proceed to monitor, repeat the test to verify critical blood glucose values, inform the physician, give insulin, and titrate the insulin drip based on the insulin management protocol. The blood glucose values entered past nurses into the EMR vital signs menstruum sheet can exist obtained (1) from the glucometer itself past manually searching the readings using the time of the test and the patient VIN to locate the exam value, (ii) from the technician, who verbally endorses the value to the nurse if he or she is bachelor in the unit of measurement, or (3) by checking the value transcribed by the technician into the paper log.
The technician docks the meter by placing it into the meter base of operations unit within 24 hours subsequently the time of the commencement examination for a given day. Meters maintain log data for up to 2000 readings. Since the meter can be docked after 24 hours of utilise, nurses usually base their insulin direction decisions on the readings transcribed past the technicians into the paper log or the readings entered by the nurses into the vital signs flow sheet. By docking the meter, readings are automatically uploaded into the RALS-Plus database, which interfaces with the EMR laboratory flow canvass. These data include the examiner'due south employee identification number, patient identification (proper noun, VIN), appointment and time of the exam, fourth dimension the meter was docked, and blood glucose values. It is worth noting that there is no straight link or seamless transfer of data in the EMR betwixt the vital signs flow sail and the EMR laboratory flow canvas.
The RALS-Plus v1.5.1 (Alere North America, LLC, Orlando, FL, USA) is a bidirectional interface software for in-hospital glucometers that uploads meter data into the EMR laboratory menstruum sheet only. The software also generates different types of reports for quality improvement. Information can be generated based on criteria such as the start and end appointment of the examination, blood glucose values, patient VIN, sample type, and examination location. Reports can be emailed, printed, saved, or exported into an Excel, rich text (rtf), or pdf file format.
Main Outcome Variables
Transcription Errors
Since the focus of this study was transcription errors, we assumed that technicians follow best practices in obtaining claret samples and in meter use according to the unit policies and procedures and the glucometer's user transmission. Blood glucose values uploaded into RALS-Plus are those in the meters and they are transferred to the EMR laboratory menses sail. These values are accurate. Transcribing blood glucose values from the meters to the paper log and the EMR vital signs flow sheet may outcome in three potential types of errors (Table 1). The "corresponding values" (Table 1) in the paper log and the EMR vital signs flow sheet are based on the same patient VIN, aforementioned date of the test, and within a one-60 minutes fourth dimension frame from the POCT (time in RALS) to the fourth dimension the test was transcribed into the paper log or the EMR vital signs flow canvass.
Table ane
Types of errors in transcribing blood glucose values from meters to the paper log and the electronic medical tape (EMR) vital signs flow sheet.
| Newspaper log | EMR vital signs menses sheet | |
| Catamenia sheet correct | Menses canvas wrong | |
| Paper right | No error: The claret glucose value in the RALS database matches the corresponding value transcribed by technicians and nurses into the paper log and the EMR vital signs menses sail for a given test in a given date and time. | Vital signs flow canvas mistake: Any discrepancy regardless of the magnitude between claret glucose value in the RALS database and the corresponding value transcribed past nurses into the EMR vital signs menstruation sheet. |
| Paper wrong | Paper log error: Whatsoever discrepancy regardless of the magnitude betwixt blood glucose value in the RALS database and the corresponding value transcribed by the technician into the paper log. | Paper log and vital signs menstruum sheet mistake: The 2 blood glucose values transcribed past technicians and nurses into the paper log and the EMR vital signs period sail for a given test in a given date and time do not match the value in the RALS database. |
Undocumented Values of Blood Glucose Tests
Untranscribed or undocumented claret glucose values are those bachelor in RALS database but were not transcribed into the newspaper log or entered into the EMR vital signs flow canvas.
Insulin Errors Related to Erroneously Transcribed Blood Glucose Values
For each transcription error, we also examined whether that error resulted in giving the wrong dose of insulin. Nosotros evaluated the wrong insulin dose based on administering a higher or lower insulin dose, regardless of the magnitude of the difference, than the 1 recommended by the protocol for the correct claret glucose value (the value in the RALS system) or not giving insulin when it should be administered to the patient according to the insulin management protocol based on the right claret glucose value.
Meter Docking Time
As mentioned above, nosotros considered a ane-hour time frame from the POCT (time in RALS or glucometer) to the fourth dimension the test result was transcribed into the paper log or the EMR vital signs menstruation sheet when we retrieved the time for transcribing blood glucose values. Meter docking fourth dimension was retrieved from the RALS database and is the time from the POCT to the fourth dimension meters were docked (readings were uploaded into the EMR laboratory flow sail).
In addition to these outcomes, we also collected patient demographics and clinical-related information such equally historic period, sex, diagnosis, diabetes status, admission and discharge dates, and total number of POCTs the patient underwent during the ICU stay.
Data Collection Procedure
We took the following steps in the sequence identified to collect the data. Three nurse educators collected the data from the paper log and the EMR vital signs flow sheet to heighten objectivity.
First, we accessed the RALS database for the selected study months and downloaded the Excel file (Microsoft Corporation). The file included the patient's name, VIN, EMR number, examination date and time, claret glucose value (meter value), and time of docking the meter.
Second, we selected a stratified sample of 20% of the blood glucose readings and the related data from RALS from the Excel file. In improver to the twenty% sample of readings, nosotros went back and selected all pertinent blood glucose tests within the episode of access for all VINs included in the stratified sample.
Third, for each test selected from RALS, we accessed the EMR and obtained patient demographics and clinical-related information based on the VIN, as well as the corresponding values of claret glucose transcribed into the vital signs period sail and time of documentation. We besides accessed the laboratory flow canvass to make certain that the tests in RALS were pertinent to that patient.
4th, for each exam selected from RALS (stride 2) for each patient and based on the VIN, we accessed the newspaper log using the patient's proper noun and VIN equally the identifiers. We obtained the corresponding blood glucose value for each exam using the date and a ane-hour time frame from the POCT (fourth dimension in RALS) equally the matching codes. We also obtained the actual time of the test documented in the paper log.
Data Assay
We used R statistical calculating software v3.5.ane (R Foundation) to analyze the information. Patients' characteristics and all types of errors were presented using descriptive statistics. We examined the departure in number of POCTs between diabetic and nondiabetic patients using Student t test with significance set at P<.05.
We limited the analysis of transcription errors to cases where the results of the blood glucose tests were transcribed past clinicians and nurses. For instance, the denominator for the paper log transcription errors was the number of blood glucose readings transcribed into the paper log, excluding missing values (ie, when the readings were not transcribed).
Results
Patient Characteristics and Number of Point-of-Intendance Tests
The 5049 claret glucose tests analyzed for transcription errors, undocumented blood glucose readings, and meter docking time were pertinent to 234 unique patients, each with a unique VIN. Tabular array two presents the patients' characteristics. Almost of the patients with documented diabetes status in the dataset did not accept diabetes (93/234). Of the 234 patients, 97 were with unknown diabetes status. The average number of POCTs performed on diabetic patients (Table 3) was significantly higher than on nondiabetic patients (t 47=–2.17, P=.03). One of the patients had 792 POCTs during his stay (Table 3). The median number of POCTs for diabetic patients was 12 tests.
Table 2
Patient characteristics (Northward=234).
| Patient feature | Statistics | |
| Age in years, hateful (SD) | 57.5 (17.4) | |
| Length of stay in days, mean (SD) | 24.8 (48.three) | |
| Number of point-of-care tests per patient, mean (SD) | 25.5 (67.9) | |
| Male sexual activity, n (%) | 131 (56.0) | |
| Diabetes status, north (%) | | |
| | Yes | 44 (32.1) |
| | No | 93 (67.9) |
| | Missing | 97 (41.5) |
Tabular array three
Comparison of the number of point-of-care tests between diabetic and nondiabetic patients.
| Diabetes status | Minimum | Median | Mean (SD) | Maximum |
| Diabetes (n=44) | 1 | 12 | 60 (126) | 792 |
| No diabetes (n=93) | one | half-dozen | 19 (40) | 344 |
Missing Documentation and Transcription Errors
Table 4 describes the number of tests that were non transcribed into the newspaper log or the EMR vital signs period sheet, or both, as well as the number of transcription errors. In the vital signs flow sheet, forty.88% of the tests (2064/5049 tests) were non transcribed. Of the blood glucose tests, 4.73% (239/5049 tests) were not transcribed in the paper log and in the EMR vital signs menstruum canvas at the same time.
Table 4
Number of undocumented claret glucose tests and number of transcription errors among the 5049 tests analyzed.
| Source | Undocumented tests, n (%) | Tests analyzed for errors, n | Errors among tests analyzed, n (%) | Range of error, mg/dL (mmol/50)a | Error rate per patient (Due north=234) |
| Paper log | 608 (12.04) | 4441 | 98 (ii.21) | –92 to 92 (–5.one to 5.1) | 0.four (98/234) |
| Vital signs catamenia sheet | 2064 (40.88) | 2985 | 242 (8.xi) | –110 to eighty (–half dozen.1 to 4.4) | 1.0 (242/234) |
| Both | 239 (4.73) | 2616 | 43 (1.64) | North/Ab | 0.two (43/234) |
Nosotros analyzed all types of transcription errors when the blood glucose value was transcribed (Tabular array 4). Of the transcription errors among the 4441 transcribed tests in the paper logs, at that place were 98 (2.21%) errors. These errors were related to xxx of the 234 patients (12.8%). Of the 2985 transcribed values in the vital signs flow sheet, there were 242 (8.11%) errors related to 63 of the 234 patients (26.9%). The total number of paper log and vital signs flow sheet transcription errors amidst the 2616 tests analyzed was 43 (1.64%), related to 24 of the 234 patients (10.3%). Overall, among the 234 patients, there were 68 (29.1%) unique patients involved in all types of errors.
Errors in the paper log resulted in transcribing a blood glucose value that was up to 92 mg/dL (5.1 mmol/L) lower or 92 mg/dL (5.1 mmol/L) college than the correct value (the one in the EMR laboratory flow sheet or RALS). However, most errors, those between the 25th and 75th percentiles, were 12 mg/dL (0.7 mmol/L) lower to vii mg/dL (0.4 mmol/L) higher than the accurate value. In the EMR vital signs period sail, the difference between the right blood glucose value and the erroneously transcribed value was 110 mg/dL (6.1 mmol/Fifty) lower to 80 mg/dL (four.iv mmol/L) higher. About errors, those between the 25th and 75th percentiles, were 3 mg/dL (0.sixteen mmol/Fifty) lower to iv mg/dL (0.two mmol/L) higher than the accurate values.
There were no meaning differences in the number of transcription errors betwixt the mean solar day shift and night shift (Table 5).
Table 5
Difference in transcription errors betwixt the twenty-four hour period shift and night shift.
| Source | Total errors, n (%) | Solar day shift, north (%) | Night shift, n (%) | Chi-square | df | P value |
| Paper log | 98/4441 (2.21) | 53/2790 (i.90) | 45/1651 (ii.73) | ii.ix | 1 | .09 |
| Vital signs menses sheet | 242/2985 (viii.11) | 163/1847 (8.83) | 79/1138 (6.94) | 3.1 | ane | .08 |
| Both | 43/2616 (one.64) | 24/1639 (1.46) | 19/977 (one.94) | 0.6 | i | .44 |
Insulin Errors
The 242 transcription errors in the EMR vital signs flow sheet resulted in 24 insulin errors. These errors resulted in giving 10 U lower to three U higher insulin dose than the dose that should have been given had at that place been no transcription errors. The 98 transcription errors in the paper log resulted in 8 insulin errors and giving 2 to 8 U lower insulin dose than the dose that should accept been given had in that location been no transcription errors. The 43 errors in the EMR vital signs menstruation sheet and paper logs resulted in ii insulin errors, both with two U lower than the correct insulin dose. Overall, there were xxx unique insulin errors that affected 25 of the 234 patients (10.vii%).
Documentation Fourth dimension
The average time from the POCT to the fourth dimension meters were docked (readings were uploaded into the EMR laboratory flow sheet) was 8 hours with a median of v.5 hours. Most readings, between the first and the third quartiles, took 1.3 to 12 hours to be uploaded into the EMR laboratory catamenia sail. Some of the readings took 56 hours (2.three days) to be uploaded into the EMR laboratory flow sheet.
In improver to these outcomes, we found xl readings that were documented to some patients' EMRs and the paper log after the date of belch.
Word
Primary Findings
This study examined transcription errors of claret glucose tests obtained by a glucometer and documented in the paper log by ICU technicians and in the EMR vital signs catamenia sheet by ICU nurses. Insulin errors resulted from transcription errors of blood glucose values, the number of undocumented blood glucose tests in the paper log and the EMR vital signs flow sail, and the average meter docking time. Research on the employ of glucometers in ICU and not-ICU settings is extensive. However, most of these studies focused on precision and accuracy of the glucometers, sources of glucometer measurement errors, and the difference in sensitivity and specificity between glucometer devices from different vendors [16-29]. Nevertheless, glucometers are unremarkably used handheld devices to measure out blood glucose at the indicate of care, specifically in ICUs to inform timely clinical decisions regarding insulin therapy. To our noesis, this is the first written report to examine transcription errors of blood glucose tests obtained by glucometers and to focus on the urgent demand for EMR-glucometer interoperability.
Transcription errors ranged from 2% for newspaper log errors to 8% for vital signs flow sheet errors. These errors resulted in a total of 30 insulin errors and affected 11% of the patients. The higher per centum of transcription errors in the vital signs menses sheet than in the newspaper log might be explained by a clinical workflow that has nurses obtain the results of blood glucose tests from three different sources, which are the paper log, the technicians, or the glucometers, while the technicians obtain the values only from the glucometers. Transcription errors in the vital signs flow sheet are clinically more significant than transcription errors in the paper log considering they inform nurses' insulin direction decisions. These errors affected 63 (27%) patients.
It is important to note that we examined transcription errors and the associated insulin errors only when the blood glucose test results were transcribed past technicians and nurses. The very high percentage of untranscribed values (ie, up to 41% untranscribed into the vital signs menstruum canvass, n=2064) could mask the actual rate of transcription errors. Possible explanations for not transcribing blood glucose values might be workload issues and the assumption that all readings eventually will be available in the laboratory flow sail in the EMR afterward docking the meter. In add-on, finding 40 readings documented to some patients' EMRs and the paper log after the appointment of discharge is alarming. This ways that technicians were non scanning the patient bracelet but probably a sticker that remained on the paper log or the patient monitor or bed. Although eliminating the utilize of a newspaper log via a full EMR-glucometer interoperability could decrease this error, adherence to the unit policies and procedures for safe testing is critical for complete elimination of this mistake.
Although a partial interface exists in our hospital between glucometers and the EMR through the RALS bidirectional interface software, this interface transfers the data only to the EMR central laboratory flow sheet. Additionally, based on the unit policies and procedures, the meters should be docked within 24 hours by technicians. This long time period hinders the availability of the tests' values at the point of care, making these data unusable for immediate clinical decisions. Furthermore, our results showed that, in reality, docking the meters might take up to more than than two days. Therefore, there is an urgent need for full glucometer-EMR connectivity to allow for seamless transfer of meter data into other fields of the EMR (ie, the vital signs menses canvas) in order to eliminate data transcription errors and the associated insulin errors.
The few available studies on medical devices-EMR connectivity have focused on vital signs monitors in ICUs and supported improved efficiency and emptying of transcription errors when vital signs monitoring devices are continued to the EMR [14]. The results of our study back up the urgent need for a comprehensive and instant connectivity to transfer glucometer information to all fields of the EMR to better inform clinical decisions and eliminate insulin errors associated with transcription errors. On the other hand, from an engineering perspective, interoperability challenges do exist. These may include lack of research describing successes and challenges, the complexity of data elements, and the departure in type of information and formats in which information is stored and displayed. Virtually of import, studies supported the potential for new types of errors in device connectivity, such as transferring the information into the wrong patient's EMR, in addition to the deadening speed of the interface attributed to the slow speed of older medical devices and computers [12]. Therefore, the procedure of and errors associated with interoperability should be carefully examined.
Limitations
The results of this study should exist interpreted in low-cal of the post-obit limitations. First, since workload, admission rate, and the large number of monthly POCTs are inherent factors that may affect transcription errors, our results tin can only be generalized to STICUs with a like workload and charge per unit of POCTs. Second, we limited the errors examined in this report to transcription errors; measurement errors of blood glucose values that may upshot from inappropriate testing or scanning the wrong patients were beyond the telescopic of this report. Third, because we collected retrospective information, our risk cess was express to identifying the number of insulin errors resulting from transcription errors without identifying the clinical consequences or agin events of insulin errors. On the other mitt, insulin is a high-alarm medication and errors in its administration may cause serious hypoglycemia and hyperglycemia, seizures, coma, ketoacidosis, and even death [30].
Conclusions
Transcription errors of blood glucose values obtained by glucometers exercise exist and upshot in insulin errors. Given the loftier dependence on glucometers for POCTs of blood glucose in ICUs, full EMR-glucometer interoperability is required for complete and authentic documentation of claret glucose values, and elimination of transcription errors and the subsequent insulin-related errors in ICUs.
Acknowledgments
This projection was funded by the University Wellness System's Center for Clinical Excellence and the University of Texas Health School of Nursing. The role of the funding sources was limited to providing financial support for the conduct of the enquiry. Sponsors were not involved in report design; collection, analysis, or estimation of data; writing of the manuscript; or the decision to submit the article for publication.
Abbreviations
| EMR | electronic medical record |
| ICU | intensive intendance unit |
| POCT | indicate-of-care test |
| STICU | surgical trauma intensive care unit |
| VIN | visit identification number |
Footnotes
Conflicts of Interest: None declared.
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6452280/