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The aim of this study was to evaluate the effects of a data‐driven clinical productivity system that leverages Electronic Health Record (EHR) data to provide productivity decision support functionality in a real‐world clinical setting. The system was implemented for a large behavioral health care provider seeing over 75,000 distinct clients a year.

The key metric in this system is a “VPU”, which simultaneously optimizes multiple aspects of clinical care. The resulting mathematical value of clinical productivity was hypothesized to tightly link the organization's performance to its expectations and, through transparency and decision support tools at the clinician level, affect significant changes in productivity, quality, and consistency relative to traditional models of clinical productivity.

In only three months, every single variable integrated into the VPU system showed significant improvement, including a 30 percent rise in revenue, 10 percent rise in clinical percentage, a 25 percent rise in treatment plan completion, a 20 percent rise in case rate eligibility, along with similar improvements in compliance/audit issues, outcomes collection, access, etc.

A data‐driven clinical productivity system employing decision support functionality is effective, because of the impact on clinician behavior relative to traditional clinical productivity systems. Critically, the model is also extensible to integration with outcomes‐based productivity.

EHR's are only a first step – the problem is turning that data into useful information. Technology can leverage the data in order to produce actionable information that can inform clinical practice and decision‐making. Without additional technology, EHR's are essentially just copies of paper‐based records stored in electronic form.

The purpose of this paper is to show how a clinical decision support system can help in prescription and knowledge acquisition processes.

An integrated electronic medical records system (iEMRS) is designed to enhance the decision support quality in prescription.

By evaluating the system performance through 135 prescription records collected from a Hong Kong medical organization, iEMRS shows a satisfactory result in suggesting medicines that is properly the same as the decisions made by the physicians.

Compared with the static clinical guidelines built (manually) in the traditional clinical decision support system, knowledge in iEMRS is generated by the knowledge discovery result from professional experiences of various physicians and patient histories, which are more dynamic in nature. A treatment algorithm, designed in data mining technique, is introduced to improve information management in medical organizations by integration of decision support capability and EMRS, and supplement the deficiencies of traditional clinical decision support system.

The progress of initiatives concerned with implementing evaluated clinical research (such as evidence based medicine and clinical effectiveness) is dependent on the way individual health professionals actually acquire, use and value clinical knowledge in routine practice. The findings of two research projects, the Value and EVINCE projects, are compared with studies of the consolidation and application of clinical knowledge in clinical decision making. The Value project was concerned with the ways in which information from NHS libraries might be used in present and future clinical decision making. EVINCE was a similar impact study for nursing professionals. Both studies confirmed the importance of personal clinical knowledge. Health information services need to use a variety of strategies and knowledge management skills to ensure that the evaluated research evidence is assimilated and implemented into practice.

This paper provides an overview of the range and development of health informatics, with examples from the literature world wide covering the types of information involved, the areas of application, the impact of evidence based medicine and other professional issues, integrated information systems, and the needs of the public, patients and their carers. While medical informatics certainly comprises a major part of health informatics it is not the main focus of this paper. Medical informatics is the older term and involves the use of information technology and computing specifically for medical science research, and the diagnosis and treatment of disease involving, for example, X‐rays, imaging, resonance, and magnetic scanning techniques. Rather, the scope of this review is the literature relating to the wider concept of the management of information through the interdisciplinary application of information science and technology for the benefit of patients, scientists, managers, staff, and carers involved in the whole range of healthcare activity.

This paper looks at a number of approaches to health informatics that support decision‐making relevant to the integrated development and management of information systems with clinical and managerial practices in healthcare. Its main aim is to explore three such approaches for integrated development, the soft information systems and technologies methodology, participative simulation modelling and stakeholder analysis. A description of the health informatics research and development environment in the UK is given as necessary background to the paper. Organisational and social aspects are examined through these approaches including information and clinical process development, telemedicine, ethical issues of drug use and management, health policies and information management and strategies, tele‐education and modelling structures. In the conclusion the synergies between the three approaches are discussed and some principles are extracted for future research and development in integrated approaches to health informatics research.

States that risk and risk management in general practice in the UK are now integral parts of the clinical effectiveness and clinical governance agenda, rather than being primarily concerned with negligence and malpractice. Notes that this has led to the introduction of a variety of technologies for improving care and thus reducing risk. Considers the frequency, nature and causes of adverse incidents in general practice, and the rise of evidence‐based practice and clinical practice guidelines, and then looks at some of those technologies currently in use. Concludes that technology seems to have a growing impact on the practice of primary care medicine and the management of clinical risk.

The purpose of this study was to evaluate the status of US hospital Laboratory Information Systems. Laboratory Information Systems are critical to high quality healthcare service provision. Data show that the need for these systems is growing to meet accompanying technological and workload demands. Additionally, laboratory tests provide the majority of information for clinical decision‐making. Laboratory processes automation, including patient result verification, has greatly improved laboratory test throughput while decreasing turn‐around‐times, enabling critical results to reach physicians rapidly for improved clinical outcomes.

Data were drawn from the 2007 Healthcare Information and Management Systems Society (HIMSS) Analytics Database, which includes over 5,000 US healthcare organizations and provides extensive data on the hardware, software, and information technology infrastructure within healthcare organizations.

US hospitals are actively involved in laboratory systems planning to improve health service quality. Specifically, data show 76 new laboratory information systems are currently being installed in 2007 with another 399 under contract for future installation. As a result, increasing investment in laboratory information systems is providing state‐of‐the‐art clinical laboratory support, which enhances clinical care processes and improves quality. These state‐of‐the‐art Laboratory Information Systems, when linked with other clinical information systems such as Computerized Physician Order Entry and Electronic Medical Record, will support further healthcare quality improvement.

This article includes the most current information available on the US hospital laboratory information system applications.

The purpose of this paper is to develop a framework for the application of evidence-based management to chronic disease healthcare.

Chronic healthcare is specially characterized by recursive patient-physician interactions in which evidence-based medicine (EBM) is applied. However, implementing evidence-based solutions to improve healthcare quality requires managers to effect changes in many different areas: organizational structure, procedures, technology and in physician/provider behaviors. To complicate matters further, they must achieve these changes using the tools of resource allocation or incentives. The literature contains many systematic reviews evaluating the question of physician and patient behavior under various types and structures of incentives. Similarly, systematic reviews have also been done regarding specific changes to the healthcare process and their effectiveness in improving patient outcomes. Yet, these reviews uniformly lament a lack of appropriate data from well-organized studies on the question of “Why?” solutions may work in one instance while not in another. The authors present a new theoretical framework that aids in answering this question.

This paper presents a new theoretical framework (Influence Model of Chronic Healthcare) that identifies: the critical areas in which managers can effect changes that improve patient outcomes; the influence these areas can have on each other, as well as on patient and physician behavior; and the mechanisms by which these influences are exerted. For each, the authors draw upon, and present the evidence in the literature. Ultimately, the authors recognize that this is a complex question that has not yet been fully researched. The contribution of this model is twofold: first, the authors hope to focus future research efforts, and second, provide a useful heuristic to managers who must make decisions with only the lesser-quality evidence the literature contains today.

This model can be used by managers as a heuristic either ex ante or ex post to determine the effectiveness of their decisions and strategies in improving healthcare quality. In addition, it can be used to analyze why actions or decisions taken achieved a given outcome, and how best to proceed to effect further improvements on patient outcomes. Last, the model serves to focus attention on specific questions for further research.

The purpose of this paper is to evaluate the utility of clinical processes in healthcare institutions of different sizes. The implications of adoption rate of computerized physicians order entry (CPOE) and electronic medical/health records (EMRs/EHRs) in different sized healthcare institutions in the USA were studied in terms of understanding its impact on enhancement of quality of patient care.

This study has used secondary data to obtain insights on the processes and technologies used in hospitals of different sizes in the USA and enlighten those in the developing countries to adopt a strategy that would be most appropriate for them. The Dorenfest Institute for H.I.T. Research and Education Analytics database (The Dorenfest Institute, 2011) provided the data for 5,038 US hospitals. Logistic regression was performed to study the impact of the different types of processes and technologies on institutions of different sizes, classified based on the number of beds, physicians, and nurses.

The findings show that small sized hospitals had a positive relationship with drug dosing interactions process and nursing and clinician content process. On the contrary, medium sized hospitals had a negative relationship with the usage of CPOE for entering medical records, i.e. <25 percent (p<0.05). In order to be effective, these institutions should increase the usage of EMRs by more than 25 percent to get positive outcomes. Large hospitals showed a positive relationship with the usage of >75 percent of CPOE to enter medical records and usage of medical records >75 percent.

The authors demonstrate the need for an evaluation of utility of acute care hospitals based on hospital size in terms of number of physicians, and nurses, which have not been dealt earlier by the past studies. Moreover, there is also a need for an evaluation of utility of acute care hospitals for implementation of CPOEs and EMRs that are integrated with clinical decision support systems.

Although the data are US-centric, the insights provided by the results are very much relevant to the Indian scenario to support the improvement of the quality of care. The findings may help those implementing processes in healthcare institutions in India. No study has addressed the measurement of the positive and negative outcomes arising due to the implementation of different percentages of CPOEs and EMRs in different sized institutions. Further the number of physicians and nurses have not been considered earlier. Therefore, the authors have classified the hospitals based on physicians and nurses and studied their impact on the adoption of CPOEs, clinical decision support systems, and EMRs.

Proposes using the analytic concept of genre of organizational communication to study the organizational consequences of implementing clinical information systems and shifting from paper‐based to electronic patient records in clinical practices. By focusing research attention on interpersonal communication and social interaction issues not addressed in medical informatics research, this approach contributes to the understanding of organizational and institutional issues that implementing such systems may entail. The paper develops an example drawn from an in‐depth case study of a computerized order entry system to illustrate the insights this approach may provide.