Improving the normalization of complex interventions: measure development based on normalization process theory (NoMAD): study protocol

<b>Background</b> Understanding implementation processes is key to ensuring that complex interventions in healthcare are taken up in practice and thus maximize intended benefits for service provision and (ultimately) care to patients. Normalization Process Theory (NPT) provides a framework for understanding how a new intervention becomes part of normal practice. This study aims to develop and validate simple generic tools derived from NPT, to be used to improve the implementation of complex healthcare interventions.<p></p> <b>Objectives</b> The objectives of this study are to: develop a set of NPT-based measures and formatively evaluate their use for identifying implementation problems and monitoring progress; conduct preliminary evaluation of these measures across a range of interventions and contexts, and identify factors that affect this process; explore the utility of these measures for predicting outcomes; and develop an online users’ manual for the measures.<p></p> <b>Methods</b> A combination of qualitative (workshops, item development, user feedback, cognitive interviews) and quantitative (survey) methods will be used to develop NPT measures, and test the utility of the measures in six healthcare intervention settings.<p></p> <b>Discussion</b> The measures developed in the study will be available for use by those involved in planning, implementing, and evaluating complex interventions in healthcare and have the potential to enhance the chances of their implementation, leading to sustained changes in working practices

Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.http://deepblue.lib.umich.edu/bitstream/2027.42/78267/1/1748-5908-5-26.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/2/1748-5908-5-26.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/3/1748-5908-5-26-S3.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/4/1748-5908-5-26-S2.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/5/1748-5908-5-26-S1.TIFFPeer Reviewe

A National Dialogue on Health Information Technology and Privacy

Increasingly, government leaders recognize that solving the complex problems facing America today will require more than simply keeping citizens informed. Meeting challenges like rising health care costs, climate change and energy independence requires increased level of collaboration. Traditionally, government agencies have operated in silos -- separated not only from citizens, but from each other, as well. Nevertheless, some have begun to reach across and outside of government to access the collective brainpower of organizations, stakeholders and individuals.The National Dialogue on Health Information Technology and Privacy was one such initiative. It was conceived by leaders in government who sought to demonstrate that it is not only possible, but beneficial and economical, to engage openly and broadly on an issue that is both national in scope and deeply relevant to the everyday lives of citizens. The results of this first-of-its-kind online event are captured in this report, together with important lessons learned along the way.This report served as a call to action. On his first full day in office, President Obama put government on notice that this new, more collaborative model can no longer be confined to the efforts of early adopters. He called upon every executive department and agency to "harness new technology" and make government "transparent, participatory, and collaborative." Government is quickly transitioning to a new generation of managers and leaders, for whom online collaboration is not a new frontier but a fact of everyday life. We owe it to them -- and the citizens we serve -- to recognize and embrace the myriad tools available to fulfill the promise of good government in the 21st Century.Key FindingsThe Panel recommended that the Administration give stakeholders the opportunity to further participate in the discussion of heath IT and privacy through broader outreach and by helping the public to understand the value of a person-centered view of healthcare information technology

Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions

Increasing evidence has shown that theory-based health behavior change interventions are more effective than non-theory-based ones. However, only a few segments of relevant studies were theory-based, especially the studies conducted by non-psychology researchers. On the other hand, many mobile health interventions, even those based on the behavioral theories, may still fail in the absence of a user-centered design process. The gap between behavioral theories and user-centered design increases the difficulty of designing and implementing mobile health interventions. To bridge this gap, we propose a holistic approach to designing theory-based mobile health interventions built on the existing theories and frameworks of three categories: (1) behavioral theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior, and the Health Action Process Approach), (2) the technological models and frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design and Behavior Change Support System, and the Just-in-Time Adaptive Interventions), and (3) the user-centered systematic approaches (e.g., the CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic approach provides researchers a lens to see the whole picture for developing mobile health interventions

Evidence in Practice – A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand

Veterinary practitioners have extensive knowledge of animal health from their day-to-day observations of clinical patients. There have been several recent initiatives to capture these data from electronic medical records for use in national surveillance systems and clinical research. In response, an approach to surveillance has been evolving that leverages existing computerized veterinary practice management systems to capture animal health data recorded by veterinarians. Work in the United Kingdom within the VetCompass program utilizes routinely recorded clinical data with the addition of further standardized fields. The current study describes a prototype system that was developed based on this approach. In a 4-week pilot study in New Zealand, clinical data on presentation reasons and diagnoses from a total of 344 patient consults were extracted from two veterinary clinics into a dedicated database and analyzed at the population level. New Zealand companion animal and equine veterinary practitioners were engaged to test the feasibility of this national practice-based health information and data system. Strategies to ensure continued engagement and submission of quality data by participating veterinarians were identified, as were important considerations for transitioning the pilot program to a sustainable large-scale and multi-species surveillance system that has the capacity to securely manage big data. The results further emphasized the need for a high degree of usability and smart interface design to make such a system work effectively in practice. The geospatial integration of data from multiple clinical practices into a common operating picture can be used to establish the baseline incidence of disease in New Zealand companion animal and equine populations, detect unusual trends that may indicate an emerging disease threat or welfare issue, improve the management of endemic and exotic infectious diseases, and support research activities. This pilot project is an important step toward developing a national surveillance system for companion animals and equines that moves beyond emerging infectious disease detection to provide important animal health information that can be used by a wide range of stakeholder groups, including participating veterinary practices

Fuzzy Logic in Clinical Practice Decision Support Systems

Computerized clinical guidelines can provide significant benefits to health outcomes and costs, however, their effective implementation presents significant problems. Vagueness and ambiguity inherent in natural (textual) clinical guidelines is not readily amenable to formulating automated alerts or advice. Fuzzy logic allows us to formalize the treatment of vagueness in a decision support architecture. This paper discusses sources of fuzziness in clinical practice guidelines. We consider how fuzzy logic can be applied and give a set of heuristics for the clinical guideline knowledge engineer for addressing uncertainty in practice guidelines. We describe the specific applicability of fuzzy logic to the decision support behavior of Care Plan On-Line, an intranet-based chronic care planning system for General Practitioners

The Toowoomba adult trauma triage tool

Since the introduction of the Australasian Triage Scale (ATS) there has been considerable variation in its application. Improved uniformity in the application of the ATS by triage nurses is required. A reproducible, reliable and valid method to classify the illness acuity of Emergency Department patients so that a triage category 3 by one nurse means the same as a triage category 3 by another, not only in the same ED but also in another institution would be of considerable value to emergency nurses. This has been the driving motivation behind developing the Toowoomba Adult Trauma Triage Tool (TATTT). It is hoped the TATTT will support emergency nurses working in this challenging area by promoting standardisation and decreasing subjectivity in the triage decision process

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Performance-Based Financing: Report on Feasibility and Implementation Options Final September 2007

This study examines the feasibility of introducing a performance-related bonus scheme in the health sector. After describing the Tanzania health context, we define “Performance-Based Financing”, examine its rationale and review the evidence on its effectiveness. The following sections systematically assess the potential for applying the scheme in Tanzania. On the basis of risks and concerns identified, detailed design options and recommendations are set out. The report concludes with a (preliminary) indication of the costs of such a scheme and recommends a way forward for implementation. We prefer the name “Payment for Performance” or “P4P”. This is because what is envisaged is a bonus payment that is earned by meeting performance targets1. The dominant financing for health care delivery would remain grant-based as at present. There is a strong case for introducing P4P. Its main purpose will be to motivate front-line health workers to improve service delivery performance. In recent years, funding for council health services has increased dramatically, without a commensurate increase in health service output. The need to tighten focus on results is widely acknowledged. So too is the need to hold health providers more accountable for performance at all levels, form the local to the national. P4P is expected to encourage CHMTs and health facilities to “manage by results”; to identify and address local constraints, and to find innovative ways to raise productivity and reach under-served groups. As well as leveraging more effective use of all resources, P4P will provide a powerful incentive at all levels to make sure that HMIS information is complete, accurate and timely. It is expected to enhance accountability between health facilities and their managers / governing committees as well as between the Council Health Department and the Local Government Authority. Better performance-monitoring will enable the national level to track aggregate progress against goals and will assist in identifying under-performers requiring remedial action. We recommend a P4P scheme that provides a monetary team bonus, dependent on a whole facility reaching facility-specific service delivery targets. The bonus would be paid quarterly and shared equally among health staff. It should target all government health facilities at the council level, and should also reward the CHMT for “whole council” performance. All participating facilities/councils are therefore rewarded for improvement rather than absolute levels of performance. Performance indicators should not number more than 10, should represent a “balanced score card” of basic health service delivery, should present no risk of “perverse incentive” and should be readily measurable. The same set of indicators should be used by all. CHMTs would assist facilities in setting targets and monitoring performance. RHMTs would play a similar role with respect to CHMTs. The Council Health Administration would provide a “check and balance” to avoid target manipulation and verify bonus payments due. The major constraint on feasibility is the poor state of health information. Our study confirmed the findings of previous ones, observing substantial omission and error in reports from facilities to CHMTs. We endorse the conclusion of previous reviewers that the main problem lies not with HMIS design, but with its functioning. We advocate a particular focus on empowering and enabling the use of information for management by facilities and CHMTs. We anticipate that P4P, combined with a major effort in HMIS capacity building – at the facility and council level – will deliver dramatic improvements in data quality and completeness. We recommend that the first wave of participating councils are selected on the basis that they can first demonstrate robust and accurate data. We anticipate that P4P for facilities will not deliver the desired benefits unless they have a greater degree of control to solve their own problems. We therefore propose - as a prior and essential condition – the introduction of petty cash imprests for all health facilities. We believe that such a measure would bring major benefits even to facilities that have not yet started P4P. It should also empower Health Facility Committees to play a more meaningful role in health service governance at the local level. We recommend to Government that P4P bonuses, as described here, are implemented across Mainland Tanzania on a phased basis. The main constraint on the pace of roll-out is the time required to bring information systems up to standard. Councils that are not yet ready to institute P4P should get an equivalent amount of money – to be used as general revenue to finance their comprehensive council health plans. We also recommend that up-to-date reporting on performance against service delivery indicators is made a mandatory requirement for all councils and is also agreed as a standard requirement for the Joint Annual Health Sector Review. P4P can also be applied on the “demand-side” – for example to encourage women to present in case of obstetric emergencies. There is a strong empirical evidence base from other countries to demonstrate that such incentives can work. We recommend a separate policy decision on whether or not to introduce demand-side incentives. In our view, they are sufficiently promising to be tried out on an experimental basis. When taken to national scale (all councils, excepting higher level hospitals), the scheme would require annual budgetary provision of about 6 billion shillings for bonus payments. This is equivalent to 1% of the national health budget, or about 3% of budgetary resources for health at the council level. We anticipate that design and implementation costs would amount to about 5 billion shillings over 5 years – the majority of this being devoted to HMIS strengthening at the facility level across the whole country