Heuristic evaluation of clinical functions in four practice management systems: a pilot study

BACKGROUND

Dentists report that the complexity and poor usability of practice management systems (PMSs) are factors in the slow adoption of computers at chairside.

METHODS

Three dental informatics researchers performed heuristic evaluations of four PMSs to identify potential usability problems that may be encountered during the clinical care process. Each researcher performed three common clinical documentation tasks on each PMS and examined the clinical user interface of each system using a published list of 10 software heuristics. An observer recorded all findings, summarized the results and totaled the heuristic violations across all programs.

RESULTS

The authors found 229 heuristic violations. Consistency and Standards, Match Between System and the Real World, and Error Prevention were the heuristics that were violated most commonly. The patterns of heuristic violations across software packages were relatively similar.

CONCLUSIONS

The findings provide an initial assessment of potential usability problems in four PMSs. The identified violations highlight aspects of dental software that may present the most significant problems to users.

CLINICAL IMPLICATIONS

Heuristic violations in PMSs may result in usability problems for dentists and other office personnel, reducing efficiency and effectiveness of a software implementation. Vendors should consider applying user-centered design methodologies to reduce the number of potential usability problems.

Cost Drivers and Resource Allocation in Military Health Care Systems

This study illustrates the feasibility of incorporating technical efficiency considerations in the funding of military hospitals and identifies the primary drivers for hospital costs. Secondary data collected for 24 U.S.-based Army hospitals and medical centers for the years 2001 to 2003 are the basis for this analysis. Technical efficiency was measured by using data envelopment analysis; subsequently, efficiency estimates were included in logarithmic-linear cost models that specified cost as a function of volume, complexity, efficiency, time, and facility type. These logarithmic-linear models were compared against stochastic frontier analysis models. A parsimonious, three-variable, logarithmic-linear model composed of volume, complexity, and efficiency variables exhibited a strong linear relationship with observed costs (R(2) = 0.98). This model also proved reliable in forecasting (R(2) = 0.96). Based on our analysis, as much as $120 million might be reallocated to improve the United States-based Army hospital performance evaluated in this study.

An artificial neural networks approach in managing healthcare

Hospitals as learning organisations have evolved through complex phases of service failures and continuous service improvement to meet the business needs of a varied continuum of care customers. This paper explores the use of Artificial Neural Network (ANN) in the development of a decision support system to manage healthcare non-clinical services. The information (postal questionnaires and repertory grid interviews) used to develop the input to the National Healthcare Service Facilities Risk Exposure System (NHSFRES) was articulated from 60 experienced healthcare operators. The system provides a reasonable early warning signal to the healthcare managers, and can be used by decision makers to evaluate the severity of risks on healthcare non clinical business operations. The advantage of using NHSFRES is that healthcare managers can provide their own risk assessment values (point score system) based on their own healthcare management business knowledge/judgement and corporate objectives.

Cost analysis in interventional radiology—A tool to optimize management costs

OBJECTIVE

The objective of the study was to analyze the methods to reduce cost in interventional radiology departments by reorganizing procurement.

MATERIALS AND METHODS

All products used in the Department of Interventional Radiology were inventoried. An ABC-analysis was completed and A-products (high-value and high turnover products) underwent a XYZ-analysis which predicted demand on the basis of ordering frequency. Then criteria for a procurement strategy for the different material categories were fixed. The net working capital (NWC) was calculated using an interest rate of 8%/year.

RESULTS

Total annual material turnover was 353,000 euro. The value of all A-products determined by the inventory was 260,000 euro. Changes in the A-product procurement strategy tapped a cost reduction potential of 14,500/year euro. The resulting total saving was 17,200 euro. Improved stores management added another 37,500 euro. The total cost cut of 52,000 euro is equivalent to 14.7% of annual expenses.

CONCLUSION

A flexible procurement strategy helps to reduce the storage and capital tie-up costs of A-products in interventional radiology without affecting the quality of service provided to patients.

An integrative health information systems approach for facilitating strategic planning in hospitals

This article presents a framework for developing strategic information systems (SISs) for hospitals. It proposes a SIS formulation process which incorporates complexity theory, strategic/organizational analysis theory, and conventional MIS development concepts. Within the formulation process, four dimensions of SIS are proposed as well as an implementation plan. A major contribution of this article is the development of a hospital SIS framework which permits an organization to fluidly respond to external, interorganizational, and intraorganizational influences. In addition, this article offers a checklist which managers can utilize in developing an SIS in health care.

[Development of medical emergency response system for accidents due to chemicals in Chongqing municipality]

OBJECTIVE

To plan and develop a Chongqing chemical accident rescue command system.

METHODS

Based on the modes of leakage and diffusion of various poisonous gases and chemicals, different modes of injuries produced, and their appropriate rescue and treatments, also taking the following factors such as the condition of storage of chemicals, meteorological and geographic conditions, medical institutions and equipment, and their rescuing capacity into consideration, a plan was drafted to establish the rescue system. Real-time simulation technology, data analysis, evaluation technology and database technology were employed in the planning. Using Visual Studio 6.0 as the software development platform, this project aimed to design the software of an emergency command system for chemical accidents in Chongqing which could be operated with the Windows 2000/XP operating system.

RESULTS

This system provided a dynamic scope of the endangered area, casualty number estimates, and recommendation of measures and a rescue plan for various chemical accidents. Furthermore, the system helped retrieve comprehensive information regarding the physical and chemical characteristics of more than 4 200 dangerous poisonous chemicals and their appropriate treatment modalities.

CONCLUSION

This system is easy to operate with a friendly interface, functions rapidly and can provide real-time analysis with comparatively precise results. This system could satisfy the requirements of executing the command and the rescue of a chemical accident with good prospects of application.

Supporting command and control training functions in the emergency management domain using cognitive systems engineering

The design and implementation of MERMAIDS, a computer-based training system in the domain of emergency command and control, is described. The research investigates the use of cognitive systems engineering and information management tools for modelling and representing training knowledge of emergency system operators. We propose a decision-centric human-computer interface as a new method of supporting computer-based modelling in the domain of emergency systems. Several interacting themes in information management relevant to emergency response planning are discussed.

National Emergency Department Overcrowding Study tool is not useful in an Australian emergency department

OBJECTIVE

To determine the accuracy and usefulness of the National Emergency Department Overcrowding Study (NEDOCS) tool in an urban hospital ED in Australia by direct comparison with subjective assessment by senior ED staff.

METHOD

A sample of simultaneous subjective and objective data pairs were collected six times a day for a period of 3 weeks. All senior medical staff in the ED answered a brief questionnaire along with the senior charge nurse for the ED. Simultaneously, the senior charge nurse also documented the total number of patients in the ED, the number of patients awaiting admission, the number of patients on ventilators, the longest time waited by an ED patient for ward bed, and the waiting time for the last patient from the Waiting Room placed on a trolley. The objective indicators were entered into a Web-based NEDOCS tool and transformed scores were compared with the averaged and transformed subjective scores for each sample time. Bland-Altmann and Kappa statistics were used to test the agreement between the objective and subjective measuring methods.

RESULTS

The mean difference between the subjective and objective methods was small (3.5 [95% confidence interval -0.875-7.878] ); however, the 95% limits of agreement was wide (-46.52-53.43). The Kappa statistic used to assess the extent of reproducibility between categorical variables was 0.31 (95% confidence interval 0.17-0.45).

CONCLUSION

The present study suggests that NEDOCS method of processing the objective overcrowding data does not accurately reflect the subjective assessment of the senior staff working at that time in the ED. This might be because the assumptions of the original NEDOCS study are flawed.

Improving imperfect data from health management information systems in Africa using space-time geostatistics

BACKGROUND

Reliable and timely information on disease-specific treatment burdens within a health system is critical for the planning and monitoring of service provision. Health management information systems (HMIS) exist to address this need at national scales across Africa but are failing to deliver adequate data because of widespread underreporting by health facilities. Faced with this inadequacy, vital public health decisions often rely on crudely adjusted regional and national estimates of treatment burdens.

METHODS AND FINDINGS

This study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that can predict values for all data that are missing through space and time. The resulting complete set can then be used to define treatment burdens for presumed malaria at any level of spatial and temporal aggregation. Validation of the model has shown that these burdens are quantified to an acceptable level of accuracy at the district, provincial, and national scale.

CONCLUSIONS

The modelling framework presented here provides, to our knowledge for the first time, reliable information from imperfect HMIS data to support evidence-based decision-making at national and sub-national levels.

Association rule discovery with the train and test approach for heart disease prediction

Association rules represent a promising technique to improve heart disease prediction. Unfortunately, when association rules are applied on a medical data set, they produce an extremely large number of rules. Most of such rules are medically irrelevant and the time required to find them can be impractical. A more important issue is that, in general, association rules are mined on the entire data set without validation on an independent sample. To solve these limitations, we introduce an algorithm that uses search constraints to reduce the number of rules, searches for association rules on a training set, and finally validates them on an independent test set. The medical significance of discovered rules is evaluated with support, confidence, and lift. Association rules are applied on a real data set containing medical records of patients with heart disease. In medical terms, association rules relate heart perfusion measurements and risk factors to the degree of disease in four specific arteries. Search constraints and test set validation significantly reduce the number of association rules and produce a set of rules with high predictive accuracy. We exhibit important rules with high confidence, high lift, or both, that remain valid on the test set on several runs. These rules represent valuable medical knowledge.

A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples

Currently, the best way to reduce the mortality of cancer is to detect and treat it in the earliest stages. Technological advances in genomics and proteomics have opened a new realm of methods for early detection that show potential to overcome the drawbacks of current strategies. In particular, pattern analysis of mass spectra of blood samples has attracted attention as an approach to early detection of cancer. Mass spectrometry provides rapid and precise measurements of the sizes and relative abundances of the proteins present in a complex biological/chemical mixture. This article presents a review of the development of clinical decision support systems using mass spectrometry from a machine learning perspective. The literature is reviewed in an explicit machine learning framework, the components of which are preprocessing, feature extraction, feature selection, classifier training, and evaluation.

A service system planning model for individuals with serious mental illness

An institutional-based care system in mental health has been replaced by a network of community-based services with different levels of structure and support. This poses both an opportunity and a challenge to provide appropriate and effective care to persons with serious mental illnesses. This paper describes a simulation-based approach for mental health system planning, focused on hospital and residential service components that can be used as a decision support tool. A key feature of this approach is the ability to represent the current service configuration of psychiatric care and the client flow pattern within that framework. The strength of the simulation model is to help mental health service managers and planners visualize the interconnected nature of client flow in their mental health system and understand possible impacts of changes in arrival rates, service times, and bed capacity on overall system performance. The planning model will assist state mental health agencies to respond to requirements of the Olmstead decision to ensure that individuals with serious mental illness receive care in the least restrictive setting. Future plans for refining the model and its application to other service systems is discussed.

From buzzword to business strategy: the case for information and decision support systems in e-healthcare

Although electronic healthcare can boast of a remarkable origin in modern-day e-commerce in the form of Electronic Data Interchange (EDI), its mission-critical nature in information-based strategising is yet to be realised. Restricting the scope of e-healthcare management to product advertisements and website management reflects an unfortunate trend of underutilisation of the scope of electronic decision support systems in pricing and other business strategies. This paper aims to illustrate how this trend can be corrected by transforming e-healthcare into a full-fledged business strategy for strategic positioning and corporate profitability. This argument is illustrated with the aid of a business example related to transfer pricing.

Developing a strategic framework for healthcare standards

This paper uses the example of information security to consider ways of ensuring that standards development matches evolving market needs within appropriate timeframes. It then considers the use of simple process maps as a way to identify interdependencies between emergent standards within specific domains, as well as generic characteristics that cross domain boundaries. It concludes by briefly examining issues that lie beyond the traditional technical orientation to consider its extension to information content and safety.

Development of SOVAT: a numerical-spatial decision support system for community health assessment research

INTRODUCTION

The development of numerical-spatial routines is frequently required to solve complex community health problems. Community health assessment (CHA) professionals who use information technology need a complete system that is capable of supporting the development of numerical-spatial routines.

BACKGROUND

Currently, there is no decision support system (DSS) that is effectively able to accomplish this task as the majority of public health geospatial information systems (GIS) are based on traditional (relational) database architecture. On-Line Analytical Processing (OLAP) is a multidimensional data warehouse technique that is commonly used as a decision support system in standard industry. OLAP alone is not sufficient for solving numerical-spatial problems that frequently occur in CHA research. Coupling it with GIS technology offers the potential for a very powerful and useful system.

METHODOLOGY

A community health OLAP cube was created by integrating health and population data from various sources. OLAP and GIS technologies were then combined to develop the Spatial OLAP Visualization and Analysis Tool (SOVAT).

RESULTS

The synergy of numerical and spatial environments within SOVAT is shown through an elaborate and easy-to-use drag and drop and direct manipulation graphical user interface (GUI). Community health problem-solving examples (routines) using SOVAT are shown through a series of screen shots.

DISCUSSION

The impact of the difference between SOVAT and existing GIS public health applications can be seen by considering the numerical-spatial problem-solving examples. These examples are facilitated using OLAP-GIS functions. These functions can be mimicked in existing GIS public applications, but their performance and system response would be significantly worse since GIS is based on traditional (relational) backend.

CONCLUSION

OLAP-GIS system offer great potential for powerful numerical-spatial decision support in community health analysis. The functionality of an OLAP-GIS system has been shown through a series of example community health numerical-spatial problems. Efforts are now focused on determining its usability during human-computer interaction (HCI). Later work will focus on performing summative evaluations comparing SOVAT to existing decision support tools used during community health assessment research.

Hillmaker: an open source occupancy analysis tool

Managerial decision making problems in the healthcare industry often involve considerations of customer occupancy by time of day and day of week. We describe an occupancy analysis tool called Hillmaker which has been used in numerous healthcare operations studies. It is being released as a free and open source software project.

Combining data mining tools with health care models for improved understanding of health processes and resource utilisation

Variability and uncertainty are inherent characteristics of most health care processes. Patient pathways and dwelling times even within the same process typically vary from patient to patient, such as the flow of patients through a particular health care provider or patient progression through the natural history of a given disease. The challenge for the OR modeller is to adequately handle and capture the stochastic features within developed models. This paper will discuss the benefits of combining patient classification tools (data mining techniques) with developed OR models, such as simulation tools, to more accurately capture patient outcomes, risks and resource needs. Illustrative applications will demonstrate the approach.

Magnet Demographic Data

The benefits of Magnet status are well documented. However, the challenges of completing the Magnet application can overextend an organization's data collection processes and systems. The authors discuss the driving forces behind development of Step One, a cost-effective on-line system alternative to manual demographic data collection. The article presents an overview of staff's role in system development and rollout and describes improvements in efficiency, accuracy, and satisfaction from data collection to final American Nurse Credentialing Center report generation.

Artificial Neural Network Medical Decision Support Tool: Predicting Transfusion Requirements of ER Patients

Blood product transfusion is a financial concern for hospitals and patients. Efficient utilization of this dwindling resource is a critical problem if hospitals are to maximize patient care while minimizing costs. Traditional statistical models do not perform well in this domain. An additional concern is the speed with which transfusion decisions and planning can be made. Rapid assessment in the emergency room (ER) necessarily limits the amount of usable information available (with respect to independent variables available). This study evaluates the efficacy of using artificial neural networks (ANNs) to predict the transfusion requirements of trauma patients using readily available information. A total of 1016 patient records are used to train and test a backpropagation neural network for predicting the transfusion requirements of these patients during the first 2, 2-6, and 6-24 h, and for total transfusions. Sensitivity and specificity analysis are used along with the mean absolute difference between blood units predicted and units transfused to demonstrate that ANNs can accurately predict most ER patient transfusion requirements, while only using information available at the time of entry into the ER.