Patient Data Management System Research Articles

Summative Software Evaluation of a Therapeutic Guideline Assistance System for Empiric Antimicrobial Therapy in ICU

While developing the patient data management system ICUData in close cooperation with the software company (IMESO GmbH, Hüttenberg, Germany), a therapeutic guideline assistance system for empiric antimicrobial therapy in ICU (called "Antibiotic Wizard") could be introduced and integrated into the existing software. After its introduction into clinical routine, the first version was to be tested, checked for usability and compared to other software products with the help of the IsoMetrics(s) inventory (based on the EN ISO 9241-10 for computer-assisted workflows). Half a year after introducing the "Antibiotic Wizard" in the ICUs, 40 physicians from different specialties at different levels of training were surveyed in order to detect deficiencies in the use of the program. The results of these surveys were compared to surveys on the word processing software Word for Windows (WinWord) from Microsoft, the hospital information system IS-H*MED from SAP (online and paper surveys) and the administrative program, SAP R/3 HR, also from SAP. Reliabilities (Cronbach's Alpha) of the subscales ranged from satisfactory (alpha > 0.70) to good (alpha > 0.80), except for "Controllability" (alpha = 0.663) and "Error tolerance" (alpha = 0.693). Medians for individual subscales ranged between 3.04 ("Error tolerance") and 3.96 ("Suitability for learning"). The "Antibiotic Wizard" showed significantly better results compared to both IS-H*MED and SAP R/3 HR in the subscales of "Suitability for the task", "Self-descriptiveness" and "Suitability for learning". In contrast, "Self-descriptiveness" "Controllability" and "Error tolerance" were significantly worse compared to WinWord. In generally, the usability of the "Antibiotic Wizard" was deemed good. Some weaknesses were found in the fields of "Error tolerance" and "Controllability". These problems will be corrected in future versions.

Impact of Different Sampling Strategies on Score Results of the Nine Equivalents of Nursing Manpower Use Score (NEMS)

Prospective observational study to assess the impact of two different sampling strategies on the score results of the NEMS, used widely to estimate the amount of nursing workload in an ICU. NEMS scores of all patients admitted to the surgical ICU over a one-year period were automatically calculated twice a day with a patient data management system for each patient day on ICU using two different sampling strategies (NEMS(individual): 24-hour intervals starting from the time of admission; NEMS(8 a.m.): 24-hour intervals starting at 8 a.m.). NEMS(individual) and NEMS(8 a.m.) were collected on 3236 patient days; 687 patients were involved. Significantly lower scores were found for the NEMS(8 a.m.) (25.0 +/- 8.7) compared to the NEMS(individual) (26.1 +/- 8.9, p < 0.01); the interclass correlation coefficient (ICC) was good but not excellent: 0.78. The inter-rater correlation between the two NEMS scores was high or very high (kappa = 0.6-1.0) for six out of nine variables of the NEMS. Different sampling strategies produce different score values, especially due to the end of stay. This has to be taken into account when using the NEMS in quality assurance projects and multi-center studies.

Predisposing factors for hypoglycemia in the intensive care unit*

The introduction of strict glycemic control in the intensive care unit has increased the risk for hypoglycemia. In this study we examined the association between predefined circumstances and the occurrence of hypoglycemia in the intensive care unit. : Retrospective cohort study. Academic medical center. All episodes of hypoglycemia (glucose value <45 mg/dL) in our intensive care unit between September 2002 and September 2004 were identified. Presence of predefined circumstances previously associated with hypoglycemia was scored around the moment of hypoglycemia using a patient data management system and in-hospital charts. Patients with a first hypoglycemic event were contrasted to controls from the same cohort, who were matched for time since admission, to correct for the effect of length of stay. Data were analyzed using conditional logistic regression analysis. None. Of 2,272 patients, 156 (6.9%) experienced at least one episode of hypoglycemia. Continuous venovenous hemofiltration with bicarbonate-based substitution fluid (odds ratio [OR], 14; 95% confidence interval [CI], 1.8-106), a decrease of nutrition without adjustment for insulin infusion (OR, 6.6; 95% CI, 1.9-23), diabetes mellitus (OR, 2.6; 95% CI, 1.5-4.7), insulin use (OR, 5.3; 95% CI, 2.8-11), sepsis (OR, 2.2; 95% CI, 1.2-4.1), and inotropic support (OR, 1.8; 95% CI, 1.1-2.9) were associated with hypoglycemia. Simultaneous octreotide and insulin use (OR, 6.0; 95% CI, 0.72-50) may also be associated with hypoglycemia. Gastric residual during enteral nutrition without adjusting insulin infusion, liver failure, continuous venovenous hemofiltration with lactate-based substitution fluid, diminished glomerular filtration rate, dose diminishment of glucocorticoids or catecholamines, and use of beta-blocking agents were not associated with hypoglycemia. Adjusting for age, gender, and Acute Physiology and Chronic Health Evaluation II score at admission did not materially change ORs. Use of bicarbonate-based substitution fluid during continuous venovenous hemofiltration, a decrease of nutrition without adjustment for insulin infusion, a prior diagnosis of diabetes mellitus, sepsis, and need for inotropic support were found to be associated with hypoglycemia. Simultaneous use of insulin and octreotide may be associated with hypoglycemia.

Introduction of the Anesthesia Patient Data Management System and Problems in the Use at Tokai University Hachioji Hospital

開院に伴い, 当院の手術室に麻酔患者データ管理システムを導入して4年が経過した. 患者情報検索が容易になるなど作業環境の向上が図られる一方で, さまざまな障害も発生している. 障害は (1)ハードの障害, (2)ソフトの障害, (3)運用上の障害, (4)操作のミスによる障害, (5)その他の障害に分類される. 導入当初は操作が不慣れな影響もあり操作のミスによるものが多かったが, 最近では減少しソフトの障害が多くを占めている. ソフトの障害の原因としては, カスタマイズしたことで麻酔記録のデータが多くなり, メモリ容量が小さくなることや, 院内電子カルテと麻酔患者データ管理システムとの互換性がほとんどないことがあげられる. しかし実際は, 障害の内訳を厳密に区別することは難しい.

Review of antibiotic drug use in a surgical ICU: Management with a patient data management system for additional outcome analysis in patients staying more than 24 hours

Background: A number of developments have been made in computerized patient data management systems (PDMSs), making them of interest to medical and nursing staff as a means of improving patient care. Objectives: The aim of this study was to assess the capability of a PDMS to record and provide drug-administration data and to investigate whether the PDMS may be used as a means of support for clinical audits and quality control. Furthermore, we assessed whether antibiotic therapy as a surrogate for infections correlates with hospital mortality in patients staying >24 hours in a surgical intensive care unit (SICU). Methods: Because of its medical and economic importance in ICU treatment, we chose to use the field of antibiotic therapy as an example. A PDMS was used in a 14-bed SICU (Department of Anesthesiology, Intensive Care Medicine, and Pain Therapy, University Hospital Giessen, Giessen, Germany) to record relevant patient data, including therapeutic, diagnostic, and nursing actions. During a 15-month period (April 1, 2000 to June 30, 2001), antibiotic drug therapy was electronically analyzed and presented using the anatomic therapeutic chemical (ATC) category for antibacterials (ATC group, J01) with daily defined doses. Furthermore, the correlation of antibiotic therapy with patient outcome (hospital mortality) was tested using logistic regression analysis. Results: A total of 2053 patients were treated in the SICU. Of these, 58.0% (1190 patients) received antibiotics (4479 treatment days; 13,145 single doses). Cephalosporins (ATC category, J01DA) were used most frequently (1785 treatment days [39.9% of treatment days]), followed by combinations of penicillins with beta-lactam inhibitors (ATC category, J01CR; 1478 treatment days [33.0%]) and imidazole derivatives (ATC category, J01XD; 667 treatment days [14.9%]). The antibiotic therapy lasted <3 days in 65.6% of cases. In 13.8% of cases, the treatment lasted >1 week. A total of 36.7% of cases were treated with only 1 antibiotic agent, 14.1% were given a combination of 2, and 7.2% were given a combination of ≥3 antibiotic agents. Seven hundred twenty-six patients remained in the SICU for >24 hours; 143 (19.7%) died during their hospital stay; 110 (15.2%) in the SICU. The duration of antibiotic therapy (odds ratio [OR], 1.46) and number of different antibiotic drugs used (OR, 2.15) significantly correlated with hospital mortality. Conclusions: Antibiotic therapy in a SICU can be assessed and analyzed in detail using a PDMS. Furthermore, in this study, the duration of antibiotic therapy and the number of antibiotic agents used correlated with hospital mortality. In further developing PDMSs, it is important for quality-assurance purposes to document the reasons for giving antibiotics and for changing prescriptions. It would also be helpful to integrate certain therapy standards and reminder functions for the duration of therapy in the PDMS.

Automatic calculation of the nine equivalents of nursing manpower use score (NEMS) using a patient data management system.

The most recent approach to estimate nursing resources consumption has led to the generation of the Nine Equivalents of Nursing Manpower use Score (NEMS). The objective of this prospective study was to establish a completely automatically generated calculation of the NEMS using a patient data management system (PDMS) database and to validate this approach by comparing the results with those of the conventional manual method. Prospective study. Operative intensive care unit of a university hospital. Patients admitted to the ICU between 24 July 2002 and 22 August 2002. Patients under the age of 16 years, and patients undergoing cardiovascular surgery or with burn injuries were excluded. None. The NEMS of all patients was calculated automatically with a PDMS and manually by a physician in parallel. The results of the two methods were compared using the Bland and Altman approach, the interclass correlation coefficient (ICC), and the kappa-statistic. On 20 consecutive working days, the NEMS was calculated in 204 cases. The Bland Altman analysis did not show significant differences in NEMS scoring between the two methods. The ICC (95% confidence intervals) 0.87 (0.84-0.90) revealed a high inter-rater agreement between the PDMS and the physician. The kappa-statistic showed good results (kappa>0.55) for all NEMS items apart from the item "supplementary ventilatory care". This study demonstrates that automatical calculation of the NEMS is possible with high accuracy by means of a PDMS. This may lead to a decrease in consumption of nursing resources.

Implementation of a patient datamanagement system in an ICU

Implementation of a patient datamanagement system in an ICU

Results and costs of intensive care in a tertiary university hospital from 1996-2000.

Costs of intensive care may be 20% of all hospital costs. Population aging likely increases the demand for intensive care services, while health care has financial limitations. Therefore data about outcome and costs of intensive care are needed. We studied changes in patient characteristics, outcome, intensity of care and costs of intensive care in a tertiary university hospital in Finland. We analyzed retrospectively data of patients admitted to the ICU between 1 January 1996 and 31 December 2000 using the patient data management system. Postoperative and ICU patients were analyzed separately. Data included age, Apache II score, cause of intensive care admission, length and intensity of ICU care. ICU, hospital and 6-month mortality were analyzed. Intensity of care was assessed by TISS points and the annual costs of intensive care were evaluated. The number of ICU admissions from 1996-2000 was 11,323. The proportions of ICU and postoperative patients were 39% and 61%, respectively. The mean age of the patients did not change. The mean Apache II score increased over time both in the ICU and postoperative patients. There was no change in crude hospital mortality. Total ICU costs decreased from 8,660,000 euros (in 1997) to 7,480,000 euros (in 2000). In the ICU patients, the costs of hospital survival decreased towards the end of the study period. We treated more severely ill patients with unchanged outcome but at lower costs towards the end of the study period. Costs of intensive care are not necessarily increasing.

Correlation of a computerized SOFA score and derived measures with length of stay at an operative ICU

The purpose of this study was to assess whether or not the information on progression of illness and therapeutic interventions included in a computerized SOFA (Sequential Organ Failure Assessment) score and derived measures (maximum SOFA, total maximum SOFA and delta SOFA) influences the correlation with length of stay (LOS) at an operative intensive care unit (ICU). During a period of one year 524 patients aging over 18 years who stayed more than 24 hours at an operative ICU were included into this study. Based on SQL scripts (Structured Query Language), a computerized SOFA score and the derived measures "maximum-SOFA", "total-maximum-SOFA" and "delta-SOFA" of all patients were retrospectively calculated for each day at ICU using routine data recorded with the patient data management system ICUData (IMESO GmbH, Hüttenberg, Germany). "Maximum-SOFA" and "total-maximum-SOFA" are measures for the severity of illness or cumulative organ dysfunction. "Delta-SOFA" measures the magnitude of organ dysfunction developing during ICU stay and is therefore potentially influenced by therapeutic measures. Analysis was based on exact LOS in hours. Mean LOS of the whole collective was 5.2 +/- 6.8 days (median 2.8, range 1 - 51). Correlation of LOS depending on score results increased from "admission-SOFA" (r = 0.280), to "maximum-SOFA" (r = 0.444), "total-maximum-SOFA" (r = 0.503), and "delta-SOFA" (r = 0.576). Therefore, the sum of information on progression of illness and therapeutic interventions included in derived measures improves the correlation with LOS at an ICU compared to the "admission-SOFA".

Computer keyboard and mouse as a reservoir of pathogens in an intensive care unit.

User interfaces of patient data management systems (PDMS) in intensive care units (ICU), like computer keyboard and mouse, may serve as reservoirs for the transmission of microorganisms. Pathogens may be transferred via the hands of personnel to the patient causing nosocomial infections. The purpose of this study was to examine the microbial contamination of computer user interfaces with potentially pathogenic microorganisms, compared with other fomites in a surgical intensive care unit of a tertiary teaching hospital. Sterile swab samples were received from patient's bedside computer keyboard and mouse, and three other sites (infusion pumps, ventilator, ward round trolley) in the patient's room in a 14 bed surgical intensive care unit at a university hospital. At the central ward samples from keyboard and mouse of the physician's workstation, and control buttons of the ward's intercom and telephone receiver were obtained. Quantitative and qualitative bacteriological sampling occurred during two periods of three months each on eight nonconsecutive days. In all 14 patients' rooms we collected a total of 1118 samples: 222 samples from keyboards and mice, 214 from infusion pumps and 174 from the ward's trolley. From the central ward 16 samples per formites were obtained (computer keyboard and mouse at the physician's workstation and the ward's intercom and telephone receiver). Microbacterial analysis from samples in patients' rooms yielded 26 contaminated samples from keyboard and mouse (5.9%) compared with 18 positive results from other fomites within patients' rooms (3.0%; p < 0.02). At the physician's computer terminal two samples obtained from the mouse (6.3%) showed positive microbial testing whereas the ward's intercom and telephone receiver were not contaminated (p = 0.15). The colonization rate for computer keyboard and mouse of a PDMS with potentially pathogenic microorganisms is greater than that of other user interfaces in a surgical ICU. These fomites may be additional reservoirs for the transmision of microorganisms and become vectors for cross-transmission of nosocomial infections in the ICU setting.

Requirements for the successful installation of an data management system

Due to increasing requirements on medical documentation, especially with reference to the German Social Law binding towards quality management and introducing a new billing system (DRGs), an increasing number of departments consider to implement a patient data management system (PDMS). The installation should be professionally planned as a project in order to insure and complete a successful installation. The following aspects are essential: composition of the project group, definition of goals, finance, networking, space considerations, hardware, software, configuration, education and support. Project and finance planning must be prepared before beginning the project and the project process must be constantly evaluated. In selecting the software, certain characteristics should be considered: use of standards, configurability, intercommunicability and modularity. Our experience has taught us that vaguely defined goals, insufficient project planning and the existing management culture are responsible for the failure of PDMS installations. The software used tends to play a less important role.

EDV im rauen Alltag der Intensivstation: Erfahrungsbericht über die Einführung des „Intensive Care Manager” (ICM, Dräger Medical, Lübeck)

Experience with implementation and operation of a patient data management system on an intensive care unit. Patient data management system "Intensive Care Manager" (ICM, Dräger Medical). Anaesthesiologic ICU (surgical patients only) of an academic teaching hospital. ICM was installed without problems and is used in routine patient treatment. The system is running reliably, availability was 99.87 % during the last 8 months. Documentation has become better and more detailed with comparable time spent. More secondary diagnoses have been recorded since ICM was implemented. Special "views" related to organ systems allow to monitor organ dysfunction more easily and facilitate treatment decisions. Review of textual information needs to be further optimised. Writing of medical reports is ideally supported by ICM. Using ICM is advantageous for doctors, nurses and patients.

The incidence and risk factors for hypotension after spinal anesthesia induction: an analysis with automated data collection.

We sought to identify factors that are associated with hypotension after the induction of spinal anesthesia (SpA) by using an anesthesia information management system. Hypotension was defined as a decrease of mean arterial blood pressure of more than 30% within a 10-min interval, and relevance was defined as a therapeutic intervention with fluids or pressors within 20 min. From January 1, 1997, to August 5, 2000, data sets from 3315 patients receiving SpA were recorded on-line by using the automatic anesthesia record keeping system NarkoData. Hypotension meeting the predefined criteria occurred in 166 (5.4%) patients. Twenty-nine patient-, surgery-, and anesthesia-related variables were studied by using univariate analysis for a possible association with the occurrence of hypotension after SpA. Logistic regression with a forward stepwise algorithm was performed to identify independent variables (P < 0.05). The discriminative power of the logistic regression model was checked with a receiver operating characteristic curve. Calibration was tested with the Hosmer-Lemeshow goodness-of-fit test. The univariate analysis identified the following variables to be associated with hypotension after SpA: age, weight, height, body mass index, amount of plain bupivacaine 0.5% used for SpA, amount of colloid infusion before puncture, chronic alcohol consumption, ASA physical status, history of hypertension, urgency of surgery, surgical department, sensory block height of anesthesia, and frequency of puncture. In the multivariate analysis, independent factors for relevant hypotension after SpA consisted of three patient-related variables ("chronic alcohol consumption," odds ratio [OR] = 3.05; "history of hypertension," OR = 2.21; and the metric variable "body mass index," OR = 1.08) and two anesthesia-related variables ("sensory block height," OR = 2.32; and "urgency of surgery," OR = 2.84). The area of 0.68 (95% confidence interval, 0.63-0.72) below the receiver operating characteristic curve was significantly greater than 0.5 (P < 0.01). The goodness-of-fit test showed a good calibration of the model (H = 4.3, df = 7, P = 0.7; C = 7.3, df = 8, P = 0.51). This study contributes to the identification of patients with a high risk for hypotension after SpA induction, with the risk increasing two- or threefold with each additional risk factor. By using automated data collection, 5 (chronic alcohol consumption, history of hypertension, body mass index, sensory block height, and urgency of surgery) of 29 variables could be detected as having an association with hypotension after spinal anesthesia induction. The knowledge of these risk factors should be useful in increasing vigilance in those patients most at risk for hypotension, in allowing a more timely therapeutic intervention, or even in suggesting the use of alternative methods of spinal anesthesia, such as titrated continuous or small-dose spinal anesthesia.

Automatic calculation of a modified APACHE II score using a patient data management system (PDMS)

Objective: To investigate a fully automated and modified APACHE II score calculation exclusively based on routine data supplied by patient data management system, the ICUData, and to assess the predictive performance of this score using analysis of discrimination and calibration at an operative ICU. Method: SQL scripts (calculation programs) were developed to calculate the scores of 524 patients who stayed at the ICU between April 1st, 1999 and March 31st, 2000. The calculation programs considered unavailable data as ‘not pathological’. The main outcome measure was survival status at ICU discharge. The discriminative power on mortality of this modified APACHE II score was checked with a receiver operating characteristic (ROC) curve. Calibration was tested using the Hosmer–Lemeshow goodness-of-fit test. Results: The 459 survivors had an average APACHE score of 17.8±5.3. The score of the 65 deceased patients averaged 22.7±4.6. The area under the ROC curve of 0.790 was significantly >0.5 ( P<0.01) and had a 95% confidence interval (CI) of 0.712–0.825. The goodness-of-fit test showed a good calibration ( H=4.89, P=0.70, dof 7, C=6.96, P=0.541, dof 8). Conclusion: A prediction model based on completely automatically calculated ‘modified APACHE II scores’ can be constructed using data collected with PDMS. However, due to differences in the patient collective and methods used, the results need validation and can only be partially compared to results from other studies.

Discriminative power on mortality of a modified Sequential Organ Failure Assessment score for complete automatic computation in an operative intensive care unit.

To evaluate the discriminative power on mortality of a modified Sequential Organ Failure Assessment (SOFA) score and derived measures (maximum SOFA, total maximum SOFA, and delta SOFA) for complete automatic computation in an operative intensive care unit (ICU). Retrospective study. Operative ICU of the Department of Anesthesiology and Intensive Care Medicine. Patients admitted to the ICU from April 1, 1999, to March 31, 2000 (n = 524). Data from patients under the age of 18 yrs and patients who stayed <24 hrs were excluded. In the case of patient readmittance, only data from the patient's last stay was included in the study. None. The main outcome measure was survival status at ICU discharge. Based on Structured Query Language (SQL) scripts, a modified SOFA score for all patients who stayed in the ICU in 1 yr was calculated for each day in the ICU. Only routine data were used, which were supplied by the patient data management system. Score evaluation was modified in registering unavailable data as being not pathologic and in using a surrogate of the Glasgow Coma Scale. During the first 24 hrs, 459 survivors had an average SOFA score of 4.5 +/- 2.1, whereas the 65 deceased patients averaged 7.6 +/- 2.9 points. The area under the receiver operating characteristic (ROC) curve was 0.799 and significantly >0.5 (p <.01). A confidence interval (CI) of 95% covers the area (0.739-0.858). The maximum SOFA presented an area under the ROC of 0.922 (CI: 0.879-0.966), the total maximum SOFA of 0.921 (CI: 0.882-0.960), and the delta SOFA of 0.828 (CI: 0.763-0.893). Despite a number of differences between completely automated data sampling of SOFA score values and manual evaluation, the technique used in this study seems to be suitable for prognosis of the mortality rate during a patient's stay at an operative ICU.

Development and evaluation of VIE-PNN, a knowledge-based system for calculating the parenteral nutrition of newborn infants

Calculating the daily changing composition of parenteral nutrition for small newborn infants is troublesome and time consuming routine work in neonatal intensive care. The task needs expertise and experience and is prone to inherent calculation errors. We designed VIE-PNN (Vienna Expert System for Parenteral Nutrition of Neonates), a knowledge-based system (KBS) in order to reduce daily routine work and calculation errors. VIE-PNN was redesigned several times because the clinicians accepted the system only when it saved time. The most recent version of VIE-PNN uses an Hypertext Markup Language (HTML)-based client–server architecture and is integrated into the intranet of the local patient data management system. Since more than 3 years all parenteral nutrition plans are calculated using VIE-PNN. Evaluating the system’s performance and the users contentedness, we compared 50 nutrition plans calculated in parallel using VIE-PNN or a hand-held calculator, retrospectively analyzed more than 5000 nutrition plans stored in VIE-PNNs database and evaluated a user questionnaire. Nutrition plans were calculated in a mean time of 2.4 versus 7.1 min using VIE-PNN or the hand-held calculator. Errors and omissions in the nutrition plans were detected in 22% versus 56% and errors in the VIE-PNN’s plans occurring only with interactively changed values. Reviews of stored plans show that a mean of 4 out of 16 parameters were interactively changed. VIE-PNN was well accepted. Most important reasons for the successful operation of VIE-PNN in the daily routine work were time savings and robustness of the system.

One-thousand consecutive inguinal hernia repairs under unmonitored local anesthesia.

To evaluate the feasibility and safety of unmonitored local anesthesia (ULA) for elective open inguinal hernia repair, we made a prospective, consecutive data collection from 1000 operations on primary and recurrent hernias. Follow-up consisted of a questionnaire 1 mo after surgery and retrieval from the electronic patient data management system. In 921 ASA Group I and II and 79 ASA Group III and IV patients, the median age was 60 yr (range, 18-95 yr). ULA was converted to general anesthesia in 5 of 1000 cases, and 961 patients were discharged on the day of surgery after 95 min (median; interquartile range, 75-150); 29 patients had complications requiring surgical intervention. Within the first month, three patients died of causes unrelated to hernia surgery, and six had cardiovascular or respiratory events. The questionnaire was returned by 940 patients; 124 were dissatisfied with local anesthesia, day-case setup, or both, primarily because of intraoperative pain (n = 74; 7.8%). We conclude that open inguinal hernia repair can be conducted under ULA, regardless of comorbidity, with a small rate of deviation from day-case setup and minimal morbidity. It provides a safe alternative to other anesthetic techniques with an acceptable rate of satisfaction, but intraoperative pain relief needs improvement. Inguinal hernia repair can be safely performed under unmonitored local anesthesia with infrequent postoperative morbidity and acceptable satisfaction, but intraoperative pain may be a problem.

A data model for managing drug therapy within a patient data management system for intensive care units

The major intent of this article was to describe the design principles of the drug-therapy documentation module of the Patient Data Management System (PDMS) ICUData, in routine use at the intensive care unit (ICU) of the Department of Anesthesiology and Intensive Care Medicine at the University Hospital of Giessen, Germany, since February 1999. The new drug management system has been in routine use since March 2000. Until 8 January 2001, 1140 patients have been documented using this approach. It could be demonstrated that it was possible to transform the formerly unstructured text-based documentation into a detailed and structured model. The mediated benefit resulted in the automatic calculation of fluid balance. Further, detailed statistical analyses of therapeutic behavior in drug administration are now possible.

Evaluation of the suitability of a patient data management system for ICUs on a general ward

The development of the ICUData patient data management system (PDMS) for intensive care units (ICU), by IMESO GmbH, Hüttenberg, Germany, was based on the assumption that processes and therapies at ICU are the most complex with the highest data density compared with those in other wards. Based on experience with the system and on a survey conducted among users at our pain clinic, we evaluated whether the concept of the present software architecture, which sufficiently reproduces processes and data at an ICU, is suitable as a PDMS for general wards. The highly modular and client-centric approach of the PDMS is founded on a message-based communications architecture (HL7). In the beginning of the year 2000, the system was implemented at the pain management clinic (12 beds) of our hospital. To assess its user friendliness, we conducted a survey of medical staff ( n=14). From April 1st 2000 to August 31st 2000, all clinical and administrative data of 658 patients at the pain management clinic were recorded with the PDMS. From the start, all users had access to data and information of other connected data management systems of the hospital (e.g. patient administrative data, patient clinical data). Staff members found the system mostly useful, clearly presented, practical, and easy to learn and use. Users were relatively satisfied with stability and performance of the program but mentioned having only limited knowledge of the program's features. The need for external support during a computer crash was rated negatively. Despite the need for further usage training and improved program performance, the software architecture described seems to be a promising starting point for the construction of a PDMS for general wards.

Organizational and Implementation Issues of Patient Data Management Systems in an Intensive Care Unit

Since the National Health Service reforms were introduced, the NHS has moved towards a greater emphasis on accountability and efficiency of healthcare. These changes rely on the swift delivery of IT systems, implemented into the NHS because of the urgency to collect data to support these measures. This case study details the events surrounding the introduction of a patient data management system into an intensive care unit in a UK hospital. It shows that its implementation was complex and involved organisational issues related to the costing of healthcare, legal and purchasing requirements, systems integration, training and staff expertise, and relationships with suppliers. It is suggested that the NHS is providing an R&amp;D environment which others are benefiting from. The NHS is supporting software development activities that are not recognised, and the true costs of this task are difficult to estimate. It is also argued that introducing PDMS crystallises many different expectations making them unmanageably complex. This could also be due to PDMS being a higher order innovation that attempts to integrate information systems products and services with the core business.