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1.  Primary Care Research Team Assessment (PCRTA): development and evaluation. 
BACKGROUND: Since the early 1990s the United Kingdom (UK) Department of Health has explicitly promoted a research and development (R&D) strategy for the National Health Service (NHS). General practitioners (GPs) and other members of the primary care team are in a unique position to undertake research activity that will complement and inform the research undertaken by basic scientists and hospital-based colleagues and lead directly to a better evidence base for decision making by primary care professionals. Opportunities to engage in R&D in primary care are growing and the scope for those wishing to become involved is finally widening. Infrastructure funding for research-active practices and the establishment of a range of support networks have helped to improve the research capacity and blur some of the boundaries between academic departments and clinical practice. This is leading to a supportive environment for primary care research. There is thus a need to develop and validate nationally accepted quality standards and accreditation of performance to ensure that funders, collaborators and primary care professionals can deliver high quality primary care research. Several strategies have been described in national policy documents in order to achieve an improvement in teaching and clinical care, as well as enhancing research capacity in primary care. The development of both research practices and primary care research networks has been recognised as having an important contribution to make in enabling health professionals to devote more protected time to undertake research methods training and to undertake research in a service setting. The recognition and development of primary care research has also brought with it an emphasis on quality and standards, including an approach to the new research governance framework. PRIMARY CARE RESEARCH TEAM ASSESSMENT: In 1998, the NHS Executive South and West, and later the London Research and Development Directorate, provided funding for a pilot project based at the Royal College of General Practitioners (RCGP) to develop a scheme to accredit UK general practices undertaking primary care R&D. The pilot began with initial consultation on the development of the process, as well as the standards and criteria for assessment. The resulting assessment schedule allowed for assessment at one of two levels: Collaborative Research Practice (Level I), with little direct experience of gaining project or infrastructure funding Established Research Practice (Level II), with more experience of research funding and activity and a sound infrastructure to allow for growth in capacity. The process for assessment of practices involved the assessment of written documentation, followed by a half-day assessment visit by a multidisciplinary team of three assessors. IMPLEMENTATION--THE PILOT PROJECT: Pilot practices were sampled in two regions. Firstly, in the NHS Executive South West Region, where over 150 practices expressed an interest in participating. From these a purposive sample of 21 practices was selected, providing a range of research and service activity. A further seven practices were identified and included within the project through the East London and Essex Network of Researchers (ELENoR). Many in this latter group received funding and administrative support and advice from ELENoR in order to prepare written submissions for assessment. Some sample loss was encountered within the pilot project, which was attributable largely to conflicting demands on participants' time. Indeed, the preparation of written submissions within the South West coincided with the introduction of primary care groups (PCGs) in April 1999, which several practices cited as having a major impact on their participation in the pilot project. A final sample of 15 practices (nine in the South West and six through ELENoR) underwent assessment through the pilot project. EVALUATION: A formal evaluation of the Primary Care Research Team Assessment (PCRTA) pilot was undertaken by an independent researcher (FM). This was supplemented with feedback from the assessment team members. The qualitative aspect of the evaluation, which included face-to-face and telephone interviews with assessors, lead researchers and other practice staff within the pilot research practices, as well as members of the project management group, demonstrated a positive view of the pilot scheme. Several key areas were identified in relation to particular strengths of research practices and areas for development including: Strengths Level II practices were found to have a strong primary care team ethos in research. Level II practices tended to have a greater degree of strategic thinking in relation to research. Development areas Level I practices were found to lack a clear and explicit research strategy. Practices at both levels had scope to develop their communication processes for dissemination of research and also for patient involvement. Practices at both levels needed mechanisms for supporting professional development in research methodology. The evaluation demonstrated that practices felt that they had gained from their participation and assessors felt that the scheme had worked well. Some specific issues were raised by different respondents within the qualitative evaluation relating to consistency of interpretation of standards and also the possible overlap of the assessment scheme with other RCGP quality initiatives. NATIONAL IMPLEMENTATION OF THE PRIMARY CARE RESEARCH TEAM ASSESSMENT: The pilot project has been very successful and recommendations have been made to progress to a UK scheme. Management and review of the scheme will remain largely the same, with a few changes focusing on the assessment process and support for practices entering the scheme. Specific changes include: development of the support and mentoring role of the primary care research networks increased peer and external support and mentoring for research practices undergoing assessment development of assessor training in line with other schemes within the RCGP Assessment Network work to ensure consistency across RCGP accreditation schemes in relation to key criteria, thereby facilitating comparable assessment processes refinement of the definition of the two groups, with Level I practices referred to as Collaborators and Level II practices as Investigator-Led. The project has continued to generate much enthusiasm and support and continues to reflect current policy. Indeed, recent developments include the proposed new funding arrangements for primary care R&D, which refer to the RCGP assessment scheme and recognise it as a key component in the future R&D agenda. The assessment scheme will help primary care trusts (PCTs) and individual practices to prepare and demonstrate their approach to research governance in a systematic way. It will also provide a more explicit avenue for primary care trusts to explore local service and development priorities identified within health improvement programmes and the research priorities set nationally for the NHS.
PMCID: PMC2560501  PMID: 12049028
2.  A Multifaceted Intervention to Implement Guidelines and Improve Admission Paediatric Care in Kenyan District Hospitals: A Cluster Randomised Trial 
PLoS Medicine  2011;8(4):e1001018.
Philip Ayieko and colleagues report the outcomes of a cluster-randomized trial carried out in eight Kenyan district hospitals evaluating the effects of a complex intervention involving improved training and supervision for clinicians. They found a higher performance of hospitals assigned to the complex intervention on a variety of process of care measures, as compared to those receiving the control intervention.
Background
In developing countries referral of severely ill children from primary care to district hospitals is common, but hospital care is often of poor quality. However, strategies to change multiple paediatric care practices in rural hospitals have rarely been evaluated.
Methods and Findings
This cluster randomized trial was conducted in eight rural Kenyan district hospitals, four of which were randomly assigned to a full intervention aimed at improving quality of clinical care (evidence-based guidelines, training, job aides, local facilitation, supervision, and face-to-face feedback; n = 4) and the remaining four to control intervention (guidelines, didactic training, job aides, and written feedback; n = 4). Prespecified structure, process, and outcome indicators were measured at baseline and during three and five 6-monthly surveys in control and intervention hospitals, respectively. Primary outcomes were process of care measures, assessed at 18 months postbaseline.
In both groups performance improved from baseline. Completion of admission assessment tasks was higher in intervention sites at 18 months (mean = 0.94 versus 0.65, adjusted difference 0.54 [95% confidence interval 0.05–0.29]). Uptake of guideline recommended therapeutic practices was also higher within intervention hospitals: adoption of once daily gentamicin (89.2% versus 74.4%; 17.1% [8.04%–26.1%]); loading dose quinine (91.9% versus 66.7%, 26.3% [−3.66% to 56.3%]); and adequate prescriptions of intravenous fluids for severe dehydration (67.2% versus 40.6%; 29.9% [10.9%–48.9%]). The proportion of children receiving inappropriate doses of drugs in intervention hospitals was lower (quinine dose >40 mg/kg/day; 1.0% versus 7.5%; −6.5% [−12.9% to 0.20%]), and inadequate gentamicin dose (2.2% versus 9.0%; −6.8% [−11.9% to −1.6%]).
Conclusions
Specific efforts are needed to improve hospital care in developing countries. A full, multifaceted intervention was associated with greater changes in practice spanning multiple, high mortality conditions in rural Kenyan hospitals than a partial intervention, providing one model for bridging the evidence to practice gap and improving admission care in similar settings.
Trial registration
Current Controlled Trials ISRCTN42996612
Please see later in the article for the Editors' Summary
Editors' Summary
Background
In 2008, nearly 10 million children died in early childhood. Nearly all these deaths were in low- and middle-income countries—half were in Africa. In Kenya, for example, 74 out every 1,000 children born died before they reached their fifth birthday. About half of all childhood (pediatric) deaths in developing countries are caused by pneumonia, diarrhea, and malaria. Deaths from these common diseases could be prevented if all sick children had access to quality health care in the community (“primary” health care provided by health centers, pharmacists, family doctors, and traditional healers) and in district hospitals (“secondary” health care). Unfortunately, primary health care facilities in developing countries often lack essential diagnostic capabilities and drugs, and pediatric hospital care is frequently inadequate with many deaths occurring soon after admission. Consequently, in 1996, as part of global efforts to reduce childhood illnesses and deaths, the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF) introduced the Integrated Management of Childhood Illnesses (IMCI) strategy. This approach to child health focuses on the well-being of the whole child and aims to improve the case management skills of health care staff at all levels, health systems, and family and community health practices.
Why Was This Study Done?
The implementation of IMCI has been evaluated at the primary health care level, but its implementation in district hospitals has not been evaluated. So, for example, interventions designed to encourage the routine use of WHO disease-specific guidelines in rural pediatric hospitals have not been tested. In this cluster randomized trial, the researchers develop and test a multifaceted intervention designed to improve the implementation of treatment guidelines and admission pediatric care in district hospitals in Kenya. In a cluster randomized trial, groups of patients rather than individual patients are randomly assigned to receive alternative interventions and the outcomes in different “clusters” of patients are compared. In this trial, each cluster is a district hospital.
What Did the Researchers Do and Find?
The researchers randomly assigned eight Kenyan district hospitals to the “full” or “control” intervention, interventions that differed in intensity but that both included more strategies to promote implementation of best practice than are usually applied in Kenyan rural hospitals. The full intervention included provision of clinical practice guidelines and training in their use, six-monthly survey-based hospital assessments followed by face-to-face feedback of survey findings, 5.5 days training for health care workers, provision of job aids such as structured pediatric admission records, external supervision, and the identification of a local facilitator to promote guideline use and to provide on-site problem solving. The control intervention included the provision of clinical practice guidelines (without training in their use) and job aids, six-monthly surveys with written feedback, and a 1.5-day lecture-based seminar to explain the guidelines. The researchers compared the implementation of various processes of care (activities of patients and doctors undertaken to ensure delivery of care) in the intervention and control hospitals at baseline and 18 months later. The performance of both groups of hospitals improved during the trial but more markedly in the intervention hospitals than in the control hospitals. At 18 months, the completion of admission assessment tasks and the uptake of guideline-recommended clinical practices were both higher in the intervention hospitals than in the control hospitals. Moreover, a lower proportion of children received inappropriate doses of drugs such as quinine for malaria in the intervention hospitals than in the control hospitals.
What Do These Findings Mean?
These findings show that specific efforts are needed to improve pediatric care in rural Kenya and suggest that interventions that include more approaches to changing clinical practice may be more effective than interventions that include fewer approaches. These findings are limited by certain aspects of the trial design, such as the small number of participating hospitals, and may not be generalizable to other hospitals in Kenya or to hospitals in other developing countries. Thus, although these findings seem to suggest that efforts to implement and scale up improved secondary pediatric health care will need to include more than the production and dissemination of printed materials, further research including trials or evaluation of test programs are necessary before widespread adoption of any multifaceted approach (which will need to be tailored to local conditions and available resources) can be contemplated.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001018.
WHO provides information on efforts to reduce global child mortality and on Integrated Management of Childhood Illness (IMCI); the WHO pocket book “Hospital care for children contains guidelines for the management of common illnesses with limited resources (available in several languages)
UNICEF also provides information on efforts to reduce child mortality and detailed statistics on child mortality
The iDOC Africa Web site, which is dedicated to improving the delivery of hospital care for children and newborns in Africa, provides links to the clinical guidelines and other resources used in this study
doi:10.1371/journal.pmed.1001018
PMCID: PMC3071366  PMID: 21483712
3.  A Patient-Centered Primary Care Practice Approach Using Evidence-Based Quality Improvement: Rationale, Methods, and Early Assessment of Implementation 
Journal of General Internal Medicine  2014;29(Suppl 2):589-597.
ABSTRACT
BACKGROUND
Healthcare systems and their primary care practices are redesigning to achieve goals identified in Patient-Centered Medical Home (PCMH) models such as Veterans Affairs (VA)’s Patient Aligned Care Teams (PACT). Implementation of these models, however, requires major transformation. Evidence-Based Quality Improvement (EBQI) is a multi-level approach for supporting organizational change and innovation spread.
OBJECTIVE
To describe EBQI as an approach for promoting VA’s PACT and to assess initial implementation of planned EBQI elements.
DESIGN
Descriptive.
PARTICIPANTS
Regional and local interdisciplinary clinical leaders, patient representatives, Quality Council Coordinators, practicing primary care clinicians and staff, and researchers from six demonstration site practices in three local healthcare systems in one VA region.
INTERVENTION
EBQI promotes bottom-up local innovation and spread within top-down organizational priorities. EBQI innovations are supported by a research-clinical partnership, use continuous quality improvement methods, and are developed in regional demonstration sites.
APPROACH
We developed a logic model for EBQI for PACT (EBQI-PACT) with inputs, outputs, and expected outcomes. We describe implementation of logic model outputs over 18 months, using qualitative data from 84 key stakeholders (104 interviews from two waves) and review of study documents.
RESULTS
Nearly all implementation elements of the EBQI-PACT logic model were fully or partially implemented. Elements not fully achieved included patient engagement in Quality Councils (4/6) and consistent local primary care practice interdisciplinary leadership (4/6). Fourteen of 15 regionally approved innovation projects have been completed, three have undergone initial spread, five are prepared to spread, and two have completed toolkits that have been pretested in two to three sites and are now ready for external spread.
DISCUSSION
EBQI-PACT has been feasible to implement in three participating healthcare systems in one VA region. Further development of methods for engaging patients in care design and for promoting interdisciplinary leadership is needed.
Electronic supplementary material
The online version of this article (doi:10.1007/s11606-013-2703-y) contains supplementary material, which is available to authorized users.
doi:10.1007/s11606-013-2703-y
PMCID: PMC4070240  PMID: 24715397
quality improvement; primary care; patient-centered medical home; logic model; interdisciplinary leadership
4.  Rational Prescribing in Primary Care (RaPP): A Cluster Randomized Trial of a Tailored Intervention 
PLoS Medicine  2006;3(6):e134.
Background
A gap exists between evidence and practice regarding the management of cardiovascular risk factors. This gap could be narrowed if systematically developed clinical practice guidelines were effectively implemented in clinical practice. We evaluated the effects of a tailored intervention to support the implementation of systematically developed guidelines for the use of antihypertensive and cholesterol-lowering drugs for the primary prevention of cardiovascular disease.
Methods and Findings
We conducted a cluster-randomized trial comparing a tailored intervention to passive dissemination of guidelines in 146 general practices in two geographical areas in Norway. Each practice was randomized to either the tailored intervention (70 practices; 257 physicians) or control group (69 practices; 244 physicians). Patients started on medication for hypertension or hypercholesterolemia during the study period and all patients already on treatment that consulted their physician during the trial were included. A multifaceted intervention was tailored to address identified barriers to change. Key components were an educational outreach visit with audit and feedback, and computerized reminders linked to the medical record system. Pharmacists conducted the visits. Outcomes were measured for all eligible patients seen in the participating practices during 1 y before and after the intervention. The main outcomes were the proportions of (1) first-time prescriptions for hypertension where thiazides were prescribed, (2) patients assessed for cardiovascular risk before prescribing antihypertensive or cholesterol-lowering drugs, and (3) patients treated for hypertension or hypercholesterolemia for 3 mo or more who had achieved recommended treatment goals.
The intervention led to an increase in adherence to guideline recommendations on choice of antihypertensive drug. Thiazides were prescribed to 17% of patients in the intervention group versus 11% in the control group (relative risk 1.94; 95% confidence interval 1.49–2.49, adjusted for baseline differences and clustering effect). Little or no differences were found for risk assessment prior to prescribing and for achievement of treatment goals.
Conclusions
Our tailored intervention had a significant impact on prescribing of antihypertensive drugs, but was ineffective in improving the quality of other aspects of managing hypertension and hypercholesterolemia in primary care.
Editors' Summary
Background.
An important issue in health care is “getting research into practice,” in other words, making sure that, when evidence from research has established the best way to treat a disease, doctors actually use that approach with their patients. In reality, there is often a gap between evidence and practice.
  An example concerns the treatment of people who have high blood pressure (hypertension) and/or high cholesterol. These are common conditions, and both increase the risk of having a heart attack or a stroke. Research has shown that the risks can be lowered if patients with these conditions are given drugs that lower blood pressure (antihypertensives) and drugs that lower cholesterol. There are many types of these drugs now available. In many countries, the health authorities want family doctors (general practitioners) to make better use of these drugs. They want doctors to prescribe them to everyone who would benefit, using the type of drugs found to be most effective. When there is a choice of drugs that are equally effective, they want doctors to use the cheapest type. (In the case of antihypertensives, an older type, known as thiazides, is very effective and also very cheap, but many doctors prefer to give their patients newer, more expensive alternatives.) Health authorities have issued guidelines to doctors that address these issues. However, it is not easy to change prescribing practices, and research in several countries has shown that issuing guidelines has only limited effects.
Why Was This Study Done?
The researchers wanted—in two parts of Norway—to compare the effects on prescribing practices of what they called the “passive dissemination of guidelines” with a more active approach, where the use of the guidelines was strongly promoted and encouraged.
What Did the Researchers Do and Find?
They worked with 146 general practices. In half of them the guidelines were actively promoted. The remaining were regarded as a control group; they were given the guidelines but no special efforts were made to encourage their use. It was decided at random which practices would be in which group; this approach is called a randomized controlled trial. The methods used to actively promote use of the guidelines included personal visits to the practices by pharmacists and use of a computerized reminder system. Information was then collected on the number of patients who, when first treated for hypertension, were prescribed a thiazide. Other information collected included whether patients had been properly assessed for their level of risk (for strokes and heart attacks) before antihypertensive or cholesterol-lowering drugs were given. In addition, the researchers recorded whether the recommended targets for improvement in blood pressure and cholesterol level had been reached.
Only 11% of those patients visiting the control group of practices who should have been prescribed thiazides, according to the guidelines, actually received them. Of those seen by doctors in the practices where the guidelines were actively promoted, 17% received thiazides. According to statistical analysis, the increase achieved by active promotion is significant. Little or no differences were found for risk assessment prior to prescribing and for achievement of treatment goals.
What Do These Findings Mean?
Even in the active promotion group, the great majority of patients (83%) were still not receiving treatment according to the guidelines. However, active promotion of guidelines is more effective than simply issuing the guidelines by themselves. The study also demonstrates that it is very hard to change prescribing practices. The efforts made here to encourage the doctors to change were considerable, and although the results were significant, they were still disappointing. Also disappointing is the fact that achievement of treatment goals was no better in the active-promotion group. These issues are discussed further in a Perspective about this study (DOI: 10.1371/journal.pmed.0030229).
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030134.
• The Web site of the American Academy of Family Physicians has a page on heart disease
• The MedlinePlus Medical Encyclopedia's pages on heart diseases and vascular diseases
• Information from NHS Direct (UK National Health Service) about heart attack and stroke
• Another PLoS Medicine article has also addressed trends in thiazide prescribing
Passive dissemination of management guidelines for hypertension and hypercholesterolaemia was compared with active promotion. Active promotion led to significant improvement in antihypertensive prescribing but not other aspects of management.
doi:10.1371/journal.pmed.0030134
PMCID: PMC1472695  PMID: 16737346
5.  Electronic Health Record Impact on Work Burden in Small, Unaffiliated, Community-Based Primary Care Practices 
ABSTRACT
BACKGROUND
The use of electronic health records (EHR) is widely recommended as a means to improve the quality, safety and efficiency of US healthcare. Relatively little is known, however, about how implementation and use of this technology affects the work of clinicians and support staff who provide primary health care in small, independent practices.
OBJECTIVE
To study the impact of EHR use on clinician and staff work burden in small, community-based primary care practices.
DESIGN
We conducted in-depth field research in seven community-based primary care practices. A team of field researchers spent 9–14 days over a 4–8 week period observing work in each practice, following patients through the practices, conducting interviews with key informants, and collecting documents and photographs. Field research data were coded and analyzed by a multidisciplinary research team, using a grounded theory approach.
PARTICIPANTS
All practice members and selected patients in seven community-based primary care practices in the Northeastern US.
KEY RESULTS
The impact of EHR use on work burden differed for clinicians compared to support staff. EHR use reduced both clerical and clinical staff work burden by improving how they check in and room patients, how they chart their work, and how they communicate with both patients and providers. In contrast, EHR use reduced some clinician work (i.e., prescribing, some lab-related tasks, and communication within the office), while increasing other work (i.e., charting, chronic disease and preventive care tasks, and some lab-related tasks). Thoughtful implementation and strategic workflow redesign can mitigate the disproportionate EHR-related work burden for clinicians, as well as facilitate population-based care.
CONCLUSIONS
The complex needs of the primary care clinician should be understood and considered as the next iteration of EHR systems are developed and implemented.
doi:10.1007/s11606-012-2192-4
PMCID: PMC3539023  PMID: 22926633
electronic health records; primary care; work burden; qualitative research
6.  Implementing Quality Improvement in Small, Autonomous Primary Care Practices: Implications for the Patient Centered Medical Home 
Quality in Primary Care  2011;19(5):289-300.
Background
Implementing improvement programs to enhance quality of care within primary care clinics is complex, with limited practical guidance available to help practices during the process. Understanding how improvement strategies can be implemented in primary care is timely given the recent national movement towards transforming primary care into patient-centered medical homes (PCMH). This study examined practice members’ perceptions of the opportunities and challenges associated with implementing changes in their practice.
Methods
Semi-structured interviews were conducted with a purposive sample of 56 individuals working in 16 small, community-based primary care practices. The interview consisted of open-ended questions focused on participants’ perceptions of: (1) practice vision, (2) perceived need for practice improvement, and (3) barriers that hinder practice improvement. The interviews were conducted at the participating clinics and were tape-recorded, transcribed, and content analyzed.
Results
Content analysis identified two main domains for practice improvement related to: (1) the process of care, and (2) patients’ involvement in their disease management. Examples of desired process of care changes included improvement in patient tracking/follow-up system, standardization of processes of care, and overall clinic documentations. Changes related to the patients’ involvement in their care included improving (a) health education, and (b) self care management. Among the internal barriers were: staff readiness for change, poor communication, and relationship difficulties among team members. External barriers were: insurance regulations, finances and patient health literacy.
Practice Implications
Transforming their practices to more patient-centered models of care will be a priority for primary care providers. Identifying opportunities and challenges associated with implementing change is critical for successful improvement programs. Successful strategy for enhancing the adoption and uptake of PCMH elements should leverage areas of concordance between practice members’ perceived needs and planned improvement efforts.
PMCID: PMC3313551  PMID: 22186171
primary care practice; quality improvement; qualitative analysis
7.  Return on Investment in Electronic Health Records in Primary Care Practices: A Mixed-Methods Study 
JMIR Medical Informatics  2014;2(2):e25.
Background
The use of electronic health records (EHR) in clinical settings is considered pivotal to a patient-centered health care delivery system. However, uncertainty in cost recovery from EHR investments remains a significant concern in primary care practices.
Objective
Guided by the question of “When implemented in primary care practices, what will be the return on investment (ROI) from an EHR implementation?”, the objectives of this study are two-fold: (1) to assess ROI from EHR in primary care practices and (2) to identify principal factors affecting the realization of positive ROI from EHR. We used a break-even point, that is, the time required to achieve cost recovery from an EHR investment, as an ROI indicator of an EHR investment.
Methods
Given the complexity exhibited by most EHR implementation projects, this study adopted a retrospective mixed-method research approach, particularly a multiphase study design approach. For this study, data were collected from community-based primary care clinics using EHR systems.
Results
We collected data from 17 primary care clinics using EHR systems. Our data show that the sampled primary care clinics recovered their EHR investments within an average period of 10 months (95% CI 6.2-17.4 months), seeing more patients with an average increase of 27% in the active-patients-to-clinician-FTE (full time equivalent) ratio and an average increase of 10% in the active-patients-to-clinical-support-staff-FTE ratio after an EHR implementation. Our analysis suggests, with a 95% confidence level, that the increase in the number of active patients (P=.006), the increase in the active-patients-to-clinician-FTE ratio (P<.001), and the increase in the clinic net revenue (P<.001) are positively associated with the EHR implementation, likely contributing substantially to an average break-even point of 10 months.
Conclusions
We found that primary care clinics can realize a positive ROI with EHR. Our analysis of the variances in the time required to achieve cost recovery from EHR investments suggests that a positive ROI does not appear automatically upon implementing an EHR and that a clinic’s ability to leverage EHR for process changes seems to play a role. Policies that provide support to help primary care practices successfully make EHR-enabled changes, such as support of clinic workflow optimization with an EHR system, could facilitate the realization of positive ROI from EHR in primary care practices.
doi:10.2196/medinform.3631
PMCID: PMC4288109  PMID: 25600508
return on investment in electronic health records; cost recovery from EHR implementation; ROI indicator; physician satisfaction with EHR; primary care practices
8.  Palliative care in the community: setting practice guidelines for primary care teams. 
BACKGROUND. Previous studies have demonstrated deficiencies in palliative care in the community. One method of translating the results of research into clinical practice, in order to produce more effective health care, is the development of clinical guidelines. Setting standards for such care has been performed by care teams in both hospital and hospice settings but not in primary care. AIM. This study set out to develop guidelines for primary care teams to follow in the provision of palliative care in the community using facilitated case discussions with the members of such teams, as a form of internal audit. METHOD. Five practices were randomly chosen from the family health services authority medical list. Meetings between the facilitators and primary care teams were held over a period of one year. The teams were asked to describe good aspects of care, areas of concern and suggestions to improve these, in recent cases of patient deaths. RESULTS. In total 56 cases were discussed. All practices felt that cohesive teamwork, coordinated management, early involvement of nursing staff and the identification of a key worker were essential for good terminal care. Concerns arose in clinical and administrative areas but the majority were linked to poor communication, either between patient and professionals within the primary care team or between primary and secondary care. All the positive aspects of care, concerns and suggestions were collated by the facilitators into guidelines for teams to refer to from the initial diagnosis of a terminal illness through to the patient's death and care of the relatives afterwards. CONCLUSION. Developing multidisciplinary as opposed to medical guidelines for palliative care allows primary health care teams to create standards that are acceptable to them and stimulates individuals within the teams to accept responsibility for initiating the change necessary for more effective care. The process of facilitating teams to discuss their work allows for recognition and respect of individuals' roles and more importantly provides shared ownership, an important contributory factor in the implementation of guidelines.
PMCID: PMC1239020  PMID: 7538315
9.  Instrument development, data collection, and characteristics of practices, staff, and measures in the Improving Quality of Care in Diabetes (iQuaD) Study 
Background
Type 2 diabetes is an increasingly prevalent chronic illness and an important cause of avoidable mortality. Patients are managed by the integrated activities of clinical and non-clinical members of primary care teams. This study aimed to: investigate theoretically-based organisational, team, and individual factors determining the multiple behaviours needed to manage diabetes; and identify multilevel determinants of different diabetes management behaviours and potential interventions to improve them. This paper describes the instrument development, study recruitment, characteristics of the study participating practices and their constituent healthcare professionals and administrative staff and reports descriptive analyses of the data collected.
Methods
The study was a predictive study over a 12-month period. Practices (N = 99) were recruited from within the UK Medical Research Council General Practice Research Framework. We identified six behaviours chosen to cover a range of clinical activities (prescribing, non-prescribing), reflect decisions that were not necessarily straightforward (controlling blood pressure that was above target despite other drug treatment), and reflect recommended best practice as described by national guidelines. Practice attributes and a wide range of individually reported measures were assessed at baseline; measures of clinical outcome were collected over the ensuing 12 months, and a number of proxy measures of behaviour were collected at baseline and at 12 months. Data were collected by telephone interview, postal questionnaire (organisational and clinical) to practice staff, postal questionnaire to patients, and by computer data extraction query.
Results
All 99 practices completed a telephone interview and responded to baseline questionnaires. The organisational questionnaire was completed by 931/1236 (75.3%) administrative staff, 423/529 (80.0%) primary care doctors, and 255/314 (81.2%) nurses. Clinical questionnaires were completed by 326/361 (90.3%) primary care doctors and 163/186 (87.6%) nurses. At a practice level, we achieved response rates of 100% from clinicians in 40 practices and > 80% from clinicians in 67 practices. All measures had satisfactory internal consistency (alpha coefficient range from 0.61 to 0.97; Pearson correlation coefficient (two item measures) 0.32 to 0.81); scores were generally consistent with good practice. Measures of behaviour showed relatively high rates of performance of the six behaviours, but with considerable variability within and across the behaviours and measures.
Discussion
We have assembled an unparalleled data set from clinicians reporting on their cognitions in relation to the performance of six clinical behaviours involved in the management of people with one chronic disease (diabetes mellitus), using a range of organisational and individual level measures as well as information on the structure of the practice teams and across a large number of UK primary care practices. We would welcome approaches from other researchers to collaborate on the analysis of this data.
doi:10.1186/1748-5908-6-61
PMCID: PMC3130687  PMID: 21658211
10.  Embedding effective depression care: using theory for primary care organisational and systems change 
Background
Depression and related disorders represent a significant part of general practitioners (GPs) daily work. Implementing the evidence about what works for depression care into routine practice presents a challenge for researchers and service designers. The emerging consensus is that the transfer of efficacious interventions into routine practice is strongly linked to how well the interventions are based upon theory and take into account the contextual factors of the setting into which they are to be transferred. We set out to develop a conceptual framework to guide change and the implementation of best practice depression care in the primary care setting.
Methods
We used a mixed method, observational approach to gather data about routine depression care in a range of primary care settings via: audit of electronic health records; observation of routine clinical care; and structured, facilitated whole of organisation meetings. Audit data were summarised using simple descriptive statistics. Observational data were collected using field notes. Organisational meetings were audio taped and transcribed. All the data sets were grouped, by organisation, and considered as a whole case. Normalisation Process Theory (NPT) was identified as an analytical theory to guide the conceptual framework development.
Results
Five privately owned primary care organisations (general practices) and one community health centre took part over the course of 18 months. We successfully developed a conceptual framework for implementing an effective model of depression care based on the four constructs of NPT: coherence, which proposes that depression work requires the conceptualisation of boundaries of who is depressed and who is not depressed and techniques for dealing with diffuseness; cognitive participation, which proposes that depression work requires engagement with a shared set of techniques that deal with depression as a health problem; collective action, which proposes that agreement is reached about how care is organised; and reflexive monitoring, which proposes that depression work requires agreement about how depression work will be monitored at the patient and practice level. We describe how these constructs can be used to guide the design and implementation of effective depression care in a way that can take account of contextual differences.
Conclusions
Ideas about what is required for an effective model and system of depression care in primary care need to be accompanied by theoretically informed frameworks that consider how these can be implemented. The conceptual framework we have presented can be used to guide organisational and system change to develop common language around each construct between policy makers, service users, professionals, and researchers. This shared understanding across groups is fundamental to the effective implementation of change in primary care for depression.
doi:10.1186/1748-5908-5-62
PMCID: PMC2925331  PMID: 20687962
11.  Barriers and Facilitators to Evidence-based Blood Pressure Control in Community Practice 
Journal of the American Board of Family Medicine : JABFM  2013;26(5):10.3122/jabfm.2013.05.130060.
Introduction
The Electronic Communications and Home Blood Pressure Monitoring trial (e-BP) demonstrated that team care incorporating a pharmacist to manage hypertension using secure E-mail with patients resulted in almost twice the rate of blood pressure (BP) control compared with usual care. To translate e-BP into community practices, we sought to identify contextual barriers and facilitators to implementation.
Methods
Interviews were conducted with medical providers, staff, pharmacists, and patients associated with community-based primary care clinics whose physician leaders had expressed interest in implementing e-BP. Transcripts were analyzed using qualitative template analysis, incorporating codes derived from the Consolidated Framework for Implementation Research (CFIR).
Results
Barriers included incorporating an unfamiliar pharmacist into the health care team, lack of information technology resources, and provider resistance to using a single BP management protocol. Facilitators included the intervention’s perceived potential to improve quality of care, empower patients, and save staff time. Sustainability of the intervention emerged as an overarching theme.
Conclusion
A qualitative approach to planning for translation is recommended to gain an understanding of contexts and to collaborate to adapt interventions through iterative, bidirectional information gathering. Interviewees affirmed that web pharmacist care offers small primary care practices a means to expand their workforce and provide patient-centered care. Reproducing e-BP in these practices will be challenging, but our interviewees expressed eagerness to try and were optimistic that a tailored intervention could succeed.
doi:10.3122/jabfm.2013.05.130060
PMCID: PMC3882900  PMID: 24004706
Evidence-based Medicine; Community Medicine; Home Blood Pressure Monitoring; Primary Health Care; Qualitative Research
12.  Developing an efficient scheduling template of a chemotherapy treatment unit 
The Australasian Medical Journal  2011;4(10):575-588.
This study was undertaken to improve the performance of a Chemotherapy Treatment Unit by increasing the throughput and reducing the average patient’s waiting time. In order to achieve this objective, a scheduling template has been built. The scheduling template is a simple tool that can be used to schedule patients' arrival to the clinic. A simulation model of this system was built and several scenarios, that target match the arrival pattern of the patients and resources availability, were designed and evaluated. After performing detailed analysis, one scenario provide the best system’s performance. A scheduling template has been developed based on this scenario. After implementing the new scheduling template, 22.5% more patients can be served.
Introduction
CancerCare Manitoba is a provincially mandated cancer care agency. It is dedicated to provide quality care to those who have been diagnosed and are living with cancer. MacCharles Chemotherapy unit is specially built to provide chemotherapy treatment to the cancer patients of Winnipeg. In order to maintain an excellent service, it tries to ensure that patients get their treatment in a timely manner. It is challenging to maintain that goal because of the lack of a proper roster, the workload distribution and inefficient resource allotment. In order to maintain the satisfaction of the patients and the healthcare providers, by serving the maximum number of patients in a timely manner, it is necessary to develop an efficient scheduling template that matches the required demand with the availability of resources. This goal can be reached using simulation modelling. Simulation has proven to be an excellent modelling tool. It can be defined as building computer models that represent real world or hypothetical systems, and hence experimenting with these models to study system behaviour under different scenarios.1, 2
A study was undertaken at the Children's Hospital of Eastern Ontario to identify the issues behind the long waiting time of a emergency room.3 A 20-­‐day field observation revealed that the availability of the staff physician and interaction affects the patient wait time. Jyväskylä et al.4 used simulation to test different process scenarios, allocate resources and perform activity-­‐based cost analysis in the Emergency Department (ED) at the Central Hospital. The simulation also supported the study of a new operational method, named "triage-team" method without interrupting the main system. The proposed triage team method categorises the entire patient according to the urgency to see the doctor and allows the patient to complete the necessary test before being seen by the doctor for the first time. The simulation study showed that it will decrease the throughput time of the patient and reduce the utilisation of the specialist and enable the ordering all the tests the patient needs right after arrival, thus quickening the referral to treatment.
Santibáñez et al.5 developed a discrete event simulation model of British Columbia Cancer Agency"s ambulatory care unit which was used to study the impact of scenarios considering different operational factors (delay in starting clinic), appointment schedule (appointment order, appointment adjustment, add-­‐ons to the schedule) and resource allocation. It was found that the best outcomes were obtained when not one but multiple changes were implemented simultaneously. Sepúlveda et al.6 studied the M. D. Anderson Cancer Centre Orlando, which is a cancer treatment facility and built a simulation model to analyse and improve flow process and increase capacity in the main facility. Different scenarios were considered like, transferring laboratory and pharmacy areas, adding an extra blood draw room and applying different scheduling techniques of patients. The study shows that by increasing the number of short-­‐term (four hours or less) patients in the morning could increase chair utilisation.
Discrete event simulation also helps improve a service where staff are ignorant about the behaviour of the system as a whole; which can also be described as a real professional system. Niranjon et al.7 used simulation successfully where they had to face such constraints and lack of accessible data. Carlos et al. 8 used Total quality management and simulation – animation to improve the quality of the emergency room. Simulation was used to cover the key point of the emergency room and animation was used to indicate the areas of opportunity required. This study revealed that a long waiting time, overload personnel and increasing withdrawal rate of patients are caused by the lack of capacity in the emergency room.
Baesler et al.9 developed a methodology for a cancer treatment facility to find stochastically a global optimum point for the control variables. A simulation model generated the output using a goal programming framework for all the objectives involved in the analysis. Later a genetic algorithm was responsible for performing the search for an improved solution. The control variables that were considered in this research are number of treatment chairs, number of drawing blood nurses, laboratory personnel, and pharmacy personnel. Guo et al. 10 presented a simulation framework considering demand for appointment, patient flow logic, distribution of resources, scheduling rules followed by the scheduler. The objective of the study was to develop a scheduling rule which will ensure that 95% of all the appointment requests should be seen within one week after the request is made to increase the level of patient satisfaction and balance the schedule of each doctor to maintain a fine harmony between "busy clinic" and "quiet clinic".
Huschka et al.11 studied a healthcare system which was about to change their facility layout. In this case a simulation model study helped them to design a new healthcare practice by evaluating the change in layout before implementation. Historical data like the arrival rate of the patients, number of patients visited each day, patient flow logic, was used to build the current system model. Later, different scenarios were designed which measured the changes in the current layout and performance.
Wijewickrama et al.12 developed a simulation model to evaluate appointment schedule (AS) for second time consultations and patient appointment sequence (PSEQ) in a multi-­‐facility system. Five different appointment rule (ARULE) were considered: i) Baily; ii) 3Baily; iii) Individual (Ind); iv) two patients at a time (2AtaTime); v) Variable Interval and (V-­‐I) rule. PSEQ is based on type of patients: Appointment patients (APs) and new patients (NPs). The different PSEQ that were studied in this study were: i) first-­‐ come first-­‐serve; ii) appointment patient at the beginning of the clinic (APBEG); iii) new patient at the beginning of the clinic (NPBEG); iv) assigning appointed and new patients in an alternating manner (ALTER); v) assigning a new patient after every five-­‐appointment patients. Also patient no show (0% and 5%) and patient punctuality (PUNCT) (on-­‐time and 10 minutes early) were also considered. The study found that ALTER-­‐Ind. and ALTER5-­‐Ind. performed best on 0% NOSHOW, on-­‐time PUNCT and 5% NOSHOW, on-­‐time PUNCT situation to reduce WT and IT per patient. As NOSHOW created slack time for waiting patients, their WT tends to reduce while IT increases due to unexpected cancellation. Earliness increases congestion whichin turn increases waiting time.
Ramis et al.13 conducted a study of a Medical Imaging Center (MIC) to build a simulation model which was used to improve the patient journey through an imaging centre by reducing the wait time and making better use of the resources. The simulation model also used a Graphic User Interface (GUI) to provide the parameters of the centre, such as arrival rates, distances, processing times, resources and schedule. The simulation was used to measure the waiting time of the patients in different case scenarios. The study found that assigning a common function to the resource personnel could improve the waiting time of the patients.
The objective of this study is to develop an efficient scheduling template that maximises the number of served patients and minimises the average patient's waiting time at the given resources availability. To accomplish this objective, we will build a simulation model which mimics the working conditions of the clinic. Then we will suggest different scenarios of matching the arrival pattern of the patients with the availability of the resources. Full experiments will be performed to evaluate these scenarios. Hence, a simple and practical scheduling template will be built based on the indentified best scenario. The developed simulation model is described in section 2, which consists of a description of the treatment room, and a description of the types of patients and treatment durations. In section 3, different improvement scenarios are described and their analysis is presented in section 4. Section 5 illustrates a scheduling template based on one of the improvement scenarios. Finally, the conclusion and future direction of our work is exhibited in section 6.
Simulation Model
A simulation model represents the actual system and assists in visualising and evaluating the performance of the system under different scenarios without interrupting the actual system. Building a proper simulation model of a system consists of the following steps.
Observing the system to understand the flow of the entities, key players, availability of resources and overall generic framework.
Collecting the data on the number and type of entities, time consumed by the entities at each step of their journey, and availability of resources.
After building the simulation model it is necessary to confirm that the model is valid. This can be done by confirming that each entity flows as it is supposed to and the statistical data generated by the simulation model is similar to the collected data.
Figure 1 shows the patient flow process in the treatment room. On the patient's first appointment, the oncologist comes up with the treatment plan. The treatment time varies according to the patient’s condition, which may be 1 hour to 10 hours. Based on the type of the treatment, the physician or the clinical clerk books an available treatment chair for that time period.
On the day of the appointment, the patient will wait until the booked chair is free. When the chair is free a nurse from that station comes to the patient, verifies the name and date of birth and takes the patient to a treatment chair. Afterwards, the nurse flushes the chemotherapy drug line to the patient's body which takes about five minutes and sets up the treatment. Then the nurse leaves to serve another patient. Chemotherapy treatment lengths vary from less than an hour to 10 hour infusions. At the end of the treatment, the nurse returns, removes the line and notifies the patient about the next appointment date and time which also takes about five minutes. Most of the patients visit the clinic to take care of their PICC line (a peripherally inserted central catheter). A PICC is a line that is used to inject the patient with the chemical. This PICC line should be regularly cleaned, flushed to maintain patency and the insertion site checked for signs of infection. It takes approximately 10–15 minutes to take care of a PICC line by a nurse.
Cancer Care Manitoba provided access to the electronic scheduling system, also known as "ARIA" which is comprehensive information and image management system that aggregates patient data into a fully-­‐electronic medical chart, provided by VARIAN Medical System. This system was used to find out how many patients are booked in every clinic day. It also reveals which chair is used for how many hours. It was necessary to search a patient's history to find out how long the patient spends on which chair. Collecting the snapshot of each patient gives the complete picture of a one day clinic schedule.
The treatment room consists of the following two main limited resources:
Treatment Chairs: Chairs that are used to seat the patients during the treatment.
Nurses: Nurses are required to inject the treatment line into the patient and remove it at the end of the treatment. They also take care of the patients when they feel uncomfortable.
Mc Charles Chemotherapy unit consists of 11 nurses, and 5 stations with the following description:
Station 1: Station 1 has six chairs (numbered 1 to 6) and two nurses. The two nurses work from 8:00 to 16:00.
Station 2: Station 2 has six chairs (7 to 12) and three nurses. Two nurses work from 8:00 to 16:00 and one nurse works from 12:00 to 20:00.
Station 3: Station 4 has six chairs (13 to 18) and two nurses. The two nurses work from 8:00 to 16:00.
Station 4: Station 4 has six chairs (19 to 24) and three nurses. One nurse works from 8:00 to 16:00. Another nurse works from 10:00 to 18:00.
Solarium Station: Solarium Station has six chairs (Solarium Stretcher 1, Solarium Stretcher 2, Isolation, Isolation emergency, Fire Place 1, Fire Place 2). There is only one nurse assigned to this station that works from 12:00 to 20:00. The nurses from other stations can help when need arises.
There is one more nurse known as the "float nurse" who works from 11:00 to 19:00. This nurse can work at any station. Table 1 summarises the working hours of chairs and nurses. All treatment stations start at 8:00 and continue until the assigned nurse for that station completes her shift.
Currently, the clinic uses a scheduling template to assign the patients' appointments. But due to high demand of patient appointment it is not followed any more. We believe that this template can be improved based on the availability of nurses and chairs. Clinic workload was collected from 21 days of field observation. The current scheduling template has 10 types of appointment time slot: 15-­‐minute, 1-­‐hour, 1.5-­‐hour, 2-­‐hour, 3-­‐hour, 4-­‐hour, 5-­‐hour, 6-­‐hour, 8-­‐hour and 10-­‐hour and it is designed to serve 95 patients. But when the scheduling template was compared with the 21 days observations, it was found that the clinic is serving more patients than it is designed for. Therefore, the providers do not usually follow the scheduling template. Indeed they very often break the time slots to accommodate slots that do not exist in the template. Hence, we find that some of the stations are very busy (mostly station 2) and others are underused. If the scheduling template can be improved, it will be possible to bring more patients to the clinic and reduce their waiting time without adding more resources.
In order to build or develop a simulation model of the existing system, it is necessary to collect the following data:
Types of treatment durations.
Numbers of patients in each treatment type.
Arrival pattern of the patients.
Steps that the patients have to go through in their treatment journey and required time of each step.
Using the observations of 2,155 patients over 21 days of historical data, the types of treatment durations and the number of patients in each type were estimated. This data also assisted in determining the arrival rate and the frequency distribution of the patients. The patients were categorised into six types. The percentage of these types and their associated service times distributions are determined too.
ARENA Rockwell Simulation Software (v13) was used to build the simulation model. Entities of the model were tracked to verify that the patients move as intended. The model was run for 30 replications and statistical data was collected to validate the model. The total number of patients that go though the model was compared with the actual number of served patients during the 21 days of observations.
Improvement Scenarios
After verifying and validating the simulation model, different scenarios were designed and analysed to identify the best scenario that can handle more patients and reduces the average patient's waiting time. Based on the clinic observation and discussion with the healthcare providers, the following constraints have been stated:
The stations are filled up with treatment chairs. Therefore, it is literally impossible to fit any more chairs in the clinic. Moreover, the stakeholders are not interested in adding extra chairs.
The stakeholders and the caregivers are not interested in changing the layout of the treatment room.
Given these constraints the options that can be considered to design alternative scenarios are:
Changing the arrival pattern of the patients: that will fit over the nurses' availability.
Changing the nurses' schedule.
Adding one full time nurse at different starting times of the day.
Figure 2 compares the available number of nurses and the number of patients' arrival during different hours of a day. It can be noticed that there is a rapid growth in the arrival of patients (from 13 to 17) between 8:00 to 10:00 even though the clinic has the equal number of nurses during this time period. At 12:00 there is a sudden drop of patient arrival even though there are more available nurses. It is clear that there is an imbalance in the number of available nurses and the number of patient arrivals over different hours of the day. Consequently, balancing the demand (arrival rate of patients) and resources (available number of nurses) will reduce the patients' waiting time and increases the number of served patients. The alternative scenarios that satisfy the above three constraints are listed in Table 2. These scenarios respect the following rules:
Long treatments (between 4hr to 11hr) have to be scheduled early in the morning to avoid working overtime.
Patients of type 1 (15 minutes to 1hr treatment) are the most common. They can be fitted in at any time of the day because they take short treatment time. Hence, it is recommended to bring these patients in at the middle of the day when there are more nurses.
Nurses get tired at the end of the clinic day. Therefore, fewer patients should be scheduled at the late hours of the day.
In Scenario 1, the arrival pattern of the patient was changed so that it can fit with the nurse schedule. This arrival pattern is shown Table 3. Figure 3 shows the new patients' arrival pattern compared with the current arrival pattern. Similar patterns can be developed for the remaining scenarios too.
Analysis of Results
ARENA Rockwell Simulation software (v13) was used to develop the simulation model. There is no warm-­‐up period because the model simulates day-­‐to-­‐day scenarios. The patients of any day are supposed to be served in the same day. The model was run for 30 days (replications) and statistical data was collected to evaluate each scenario. Tables 4 and 5 show the detailed comparison of the system performance between the current scenario and Scenario 1. The results are quite interesting. The average throughput rate of the system has increased from 103 to 125 patients per day. The maximum throughput rate can reach 135 patients. Although the average waiting time has increased, the utilisation of the treatment station has increased by 15.6%. Similar analysis has been performed for the rest of the other scenarios. Due to the space limitation the detailed results are not given. However, Table 6 exhibits a summary of the results and comparison between the different scenarios. Scenario 1 was able to significantly increase the throughput of the system (by 21%) while it still results in an acceptable low average waiting time (13.4 minutes). In addition, it is worth noting that adding a nurse (Scenarios 3, 4, and 5) does not significantly reduce the average wait time or increase the system's throughput. The reason behind this is that when all the chairs are busy, the nurses have to wait until some patients finish the treatment. As a consequence, the other patients have to wait for the commencement of their treatment too. Therefore, hiring a nurse, without adding more chairs, will not reduce the waiting time or increase the throughput of the system. In this case, the only way to increase the throughput of the system is by adjusting the arrival pattern of patients over the nurses' schedule.
Developing a Scheduling Template based on Scenario 1
Scenario 1 provides the best performance. However a scheduling template is necessary for the care provider to book the patients. Therefore, a brief description is provided below on how scheduling the template is developed based on this scenario.
Table 3 gives the number of patients that arrive hourly, following Scenario 1. The distribution of each type of patient is shown in Table 7. This distribution is based on the percentage of each type of patient from the collected data. For example, in between 8:00-­‐9:00, 12 patients will come where 54.85% are of Type 1, 34.55% are of Type 2, 15.163% are of Type 3, 4.32% are of Type 4, 2.58% are of Type 5 and the rest are of Type 6. It is worth noting that, we assume that the patients of each type arrive as a group at the beginning of the hourly time slot. For example, all of the six patients of Type 1 from 8:00 to 9:00 time slot arrive at 8:00.
The numbers of patients from each type is distributed in such a way that it respects all the constraints described in Section 1.3. Most of the patients of the clinic are from type 1, 2 and 3 and they take less amount of treatment time compared with the patients of other types. Therefore, they are distributed all over the day. Patients of type 4, 5 and 6 take a longer treatment time. Hence, they are scheduled at the beginning of the day to avoid overtime. Because patients of type 4, 5 and 6 come at the beginning of the day, most of type 1 and 2 patients come at mid-­‐day (12:00 to 16:00). Another reason to make the treatment room more crowded in between 12:00 to 16:00 is because the clinic has the maximum number of nurses during this time period. Nurses become tired at the end of the clinic which is a reason not to schedule any patient after 19:00.
Based on the patient arrival schedule and nurse availability a scheduling template is built and shown in Figure 4. In order to build the template, if a nurse is available and there are patients waiting for service, a priority list of these patients will be developed. They are prioritised in a descending order based on their estimated slack time and secondarily based on the shortest service time. The secondary rule is used to break the tie if two patients have the same slack. The slack time is calculated using the following equation:
Slack time = Due time - (Arrival time + Treatment time)
Due time is the clinic closing time. To explain how the process works, assume at hour 8:00 (in between 8:00 to 8:15) two patients in station 1 (one 8-­‐hour and one 15-­‐ minute patient), two patients in station 2 (two 12-­‐hour patients), two patients in station 3 (one 2-­‐hour and one 15-­‐ minute patient) and one patient in station 4 (one 3-­‐hour patient) in total seven patients are scheduled. According to Figure 2, there are seven nurses who are available at 8:00 and it takes 15 minutes to set-­‐up a patient. Therefore, it is not possible to schedule more than seven patients in between 8:00 to 8:15 and the current scheduling is also serving seven patients by this time. The rest of the template can be justified similarly.
doi:10.4066/AMJ.2011.837
PMCID: PMC3562880  PMID: 23386870
13.  The Importance of Relational Coordination and Reciprocal Learning for Chronic Illness Care within Primary Care Teams 
Health care management review  2013;38(1):20-28.
Background
Recent research from a complexity theory perspective suggests that implementation of complex models of care, such as the Chronic Care Model (CCM), requires strong relationships and learning capacities among primary care teams.
Purposes
Our primary aim was to assess the extent to which practice member perceptions of relational coordination and reciprocal learning were associated with the presence of CCM elements in community-based primary care practices.
Methodology/Approach
We used baseline measures from a cluster randomized controlled trial testing a practice facilitation intervention to implement the CCM and improve risk factor control for patients with type 2 diabetes in small primary care practices. Practice members (i.e., physicians, non-physician providers, and staff) completed baseline assessments, which included the Relational Coordination Scale, Reciprocal Learning Scale, and the Assessment of Chronic Illness Care (ACIC) survey, along with items assessing individual and clinic characteristics. To assess the association between Relational Coordination, Reciprocal Learning, and ACIC, we used a series of hierarchical linear regression models accounting for clustering of individual practice members within clinics and controlling for individual- and practice-level characteristics, and tested for mediation effects.
Findings
283 practice members from 39 clinics completed baseline measures. Relational Coordination scores were significantly and positively associated with ACIC scores (Model 1). When Reciprocal Learning was added, Relational Coordination remained a significant yet notably attenuated predictor of ACIC (Model 2). The mediation effect was significant (z = 9.3, p<.01); 24% of the association between Relational Coordination and ACIC scores was explained by Reciprocal Learning. Of the individual and practice level covariates included in Model 3, only the presence of an electronic medical record was significant; Relational Coordination and Reciprocal Learning remained significant independent predictors of ACIC.
Practice Implications
Efforts to implement complex models of care should incorporate strategies to strengthen relational coordination and reciprocal learning among team members.
doi:10.1097/HMR.0b013e3182497262
PMCID: PMC3383880  PMID: 22310483
primary care; chronic care model; relational coordination; reciprocal learning; diabetes
14.  The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview 
PLoS Medicine  2011;8(1):e1000387.
Aziz Sheikh and colleagues report the findings of their systematic overview that assessed the impact of eHealth solutions on the quality and safety of health care.
Background
There is considerable international interest in exploiting the potential of digital solutions to enhance the quality and safety of health care. Implementations of transformative eHealth technologies are underway globally, often at very considerable cost. In order to assess the impact of eHealth solutions on the quality and safety of health care, and to inform policy decisions on eHealth deployments, we undertook a systematic review of systematic reviews assessing the effectiveness and consequences of various eHealth technologies on the quality and safety of care.
Methods and Findings
We developed novel search strategies, conceptual maps of health care quality, safety, and eHealth interventions, and then systematically identified, scrutinised, and synthesised the systematic review literature. Major biomedical databases were searched to identify systematic reviews published between 1997 and 2010. Related theoretical, methodological, and technical material was also reviewed. We identified 53 systematic reviews that focused on assessing the impact of eHealth interventions on the quality and/or safety of health care and 55 supplementary systematic reviews providing relevant supportive information. This systematic review literature was found to be generally of substandard quality with regards to methodology, reporting, and utility. We thematically categorised eHealth technologies into three main areas: (1) storing, managing, and transmission of data; (2) clinical decision support; and (3) facilitating care from a distance. We found that despite support from policymakers, there was relatively little empirical evidence to substantiate many of the claims made in relation to these technologies. Whether the success of those relatively few solutions identified to improve quality and safety would continue if these were deployed beyond the contexts in which they were originally developed, has yet to be established. Importantly, best practice guidelines in effective development and deployment strategies are lacking.
Conclusions
There is a large gap between the postulated and empirically demonstrated benefits of eHealth technologies. In addition, there is a lack of robust research on the risks of implementing these technologies and their cost-effectiveness has yet to be demonstrated, despite being frequently promoted by policymakers and “techno-enthusiasts” as if this was a given. In the light of the paucity of evidence in relation to improvements in patient outcomes, as well as the lack of evidence on their cost-effectiveness, it is vital that future eHealth technologies are evaluated against a comprehensive set of measures, ideally throughout all stages of the technology's life cycle. Such evaluation should be characterised by careful attention to socio-technical factors to maximise the likelihood of successful implementation and adoption.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
There is considerable international interest in exploiting the potential of digital health care solutions, often referred to as eHealth—the use of information and communication technologies—to enhance the quality and safety of health care. Often accompanied by large costs, any large-scale expenditure on eHealth—such as electronic health records, picture archiving and communication systems, ePrescribing, associated computerized provider order entry systems, and computerized decision support systems—has tended to be justified on the grounds that these are efficient and cost-effective means for improving health care. In 2005, the World Health Assembly passed an eHealth resolution (WHA 58.28) that acknowledged, “eHealth is the cost-effective and secure use of information and communications technologies in support of health and health-related fields, including health-care services, health surveillance, health literature, and health education, knowledge and research,” and urged member states to develop and implement eHealth technologies. Since then, implementing eHealth technologies has become a main priority for many countries. For example, England has invested at least £12.8 billion in a National Programme for Information Technology for the National Health Service, and the Obama administration in the United States has committed to a US$38 billion eHealth investment in health care.
Why Was This Study Done?
Despite the wide endorsement of and support for eHealth, the scientific basis of its benefits—which are repeatedly made and often uncritically accepted—remains to be firmly established. A robust evidence-based perspective on the advantages on eHealth could help to suggest priority areas that have the greatest potential for benefit to patients and also to inform international eHealth deliberations on costs. Therefore, in order to better inform the international community, the authors systematically reviewed the published systematic review literature on eHealth technologies and evaluated the impact of these technologies on the quality and safety of health care delivery.
What Did the Researchers Do and Find?
The researchers divided eHealth technologies into three main categories: (1) storing, managing, and transmission of data; (2) clinical decision support; and (3) facilitating care from a distance. Then, implementing methods based on those developed by the Cochrane Collaboration and the NHS Service Delivery and Organisation Programme, the researchers used detailed search strategies and maps of health care quality, safety, and eHealth interventions to identify relevant systematic reviews (and related theoretical, methodological, and technical material) published between 1997 and 2010. Using these techniques, the researchers retrieved a total of 46,349 references from which they identified 108 reviews. The 53 reviews that the researchers finally selected (and critically reviewed) provided the main evidence base for assessing the impact of eHealth technologies in the three categories selected.
In their systematic review of systematic reviews, the researchers included electronic health records and picture archiving communications systems in their evaluation of category 1, computerized provider (or physician) order entry and e-prescribing in category 2, and all clinical information systems that, when used in the context of eHealth technologies, integrate clinical and demographic patient information to support clinician decision making in category 3.
The researchers found that many of the clinical claims made about the most commonly used eHealth technologies were not substantiated by empirical evidence. The evidence base in support of eHealth technologies was weak and inconsistent and importantly, there was insubstantial evidence to support the cost-effectiveness of these technologies. For example, the researchers only found limited evidence that some of the many presumed benefits could be realized; importantly, they also found some evidence that introducing these new technologies may on occasions also generate new risks such as prescribers becoming over-reliant on clinical decision support for e-prescribing, or overestimate its functionality, resulting in decreased practitioner performance.
What Do These Findings Mean?
The researchers found that despite the wide support for eHealth technologies and the frequently made claims by policy makers when constructing business cases to raise funds for large-scale eHealth projects, there is as yet relatively little empirical evidence to substantiate many of the claims made about eHealth technologies. In addition, even for the eHealth technology tools that have proven to be successful, there is little evidence to show that such tools would continue to be successful beyond the contexts in which they were originally developed. Therefore, in light of the lack of evidence in relation to improvements in patient outcomes, as well as the lack of evidence on their cost-effectiveness, the authors say that future eHealth technologies should be evaluated against a comprehensive set of measures, ideally throughout all stages of the technology's life cycle, and include socio-technical factors to maximize the likelihood of successful implementation and adoption in a given context. Furthermore, it is equally important that eHealth projects that have already been commissioned are subject to rigorous, multidisciplinary, and independent evaluation.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000387.
The authors' broader study is: Car J, Black A, Anandan C, Cresswell K, Pagliari C, McKinstry B, et al. (2008) The Impact of eHealth on the Quality and Safety of Healthcare. Available at: http://www.haps.bham.ac.uk/publichealth/cfhep/001.shtml
More information is available on the World Health Assembly eHealth resolution
The World Health Organization provides information at the Global Observatory on eHealth, as well as a global insight into eHealth developments
The European Commission provides Information on eHealth in Europe and some examples of good eHealth practice
More information is provided on NHS Connecting for Health
doi:10.1371/journal.pmed.1000387
PMCID: PMC3022523  PMID: 21267058
15.  Developing and implementing an integrated delirium prevention system of care: a theory driven, participatory research study 
Background
Delirium is a common complication for older people in hospital. Evidence suggests that delirium incidence in hospital may be reduced by about a third through a multi-component intervention targeted at known modifiable risk factors. We describe the research design and conceptual framework underpinning it that informed the development of a novel delirium prevention system of care for acute hospital wards. Particular focus of the study was on developing an implementation process aimed at embedding practice change within routine care delivery.
Methods
We adopted a participatory action research approach involving staff, volunteers, and patient and carer representatives in three northern NHS Trusts in England. We employed Normalization Process Theory to explore knowledge and ward practices on delirium and delirium prevention. We established a Development Team in each Trust comprising senior and frontline staff from selected wards, and others with a potential role or interest in delirium prevention. Data collection included facilitated workshops, relevant documents/records, qualitative one-to-one interviews and focus groups with multiple stakeholders and observation of ward practices. We used grounded theory strategies in analysing and synthesising data.
Results
Awareness of delirium was variable among staff with no attention on delirium prevention at any level; delirium prevention was typically neither understood nor perceived as meaningful. The busy, chaotic and challenging ward life rhythm focused primarily on diagnostics, clinical observations and treatment. Ward practices pertinent to delirium prevention were undertaken inconsistently. Staff welcomed the possibility of volunteers being engaged in delirium prevention work, but existing systems for volunteer support were viewed as a barrier.
Our evolving conception of an integrated model of delirium prevention presented major implementation challenges flowing from minimal understanding of delirium prevention and securing engagement of volunteers alongside practice change. The resulting Prevention of Delirium (POD) Programme combines a multi-component delirium prevention and implementation process, incorporating systems and mechanisms to introduce and embed delirium prevention into routine ward practices.
Conclusions
Although our substantive interest was in delirium prevention, the conceptual and methodological strategies pursued have implications for implementing and sustaining practice and service improvements more broadly.
Study registration
ISRCTN65924234
doi:10.1186/1472-6963-13-341
PMCID: PMC3766659  PMID: 24004917
Delirium; Prevention; Acute hospital care; Complex intervention; Implementation; Normalization process theory
16.  A randomized trial of practice facilitation to improve the delivery of chronic illness care in primary care: initial and sustained effects 
Background
Practice facilitation (PF) is an implementation strategy now commonly used in primary care settings for improvement initiatives. PF occurs when a trained external facilitator engages and supports the practice in its change efforts. The purpose of this group-randomized trial is to assess PF as an intervention to improve the delivery of chronic illness care in primary care.
Methods
A randomized trial of 40 small primary care practices who were randomized to an initial or a delayed intervention (control) group. Trained practice facilitators worked with each practice for one year to implement tailored changes to improve delivery of diabetes care within the Chronic Care Model framework. The Assessment of Chronic Illness Care (ACIC) survey was administered at baseline and at one-year intervals to clinicians and staff in both groups of practices. Repeated-measures analyses of variance were used to assess the main effects (mean differences between groups) and the within-group change over time.
Results
There was significant improvement in ACIC scores (p < 0.05) within initial intervention practices, from 5.58 (SD 1.89) to 6.33 (SD 1.50), compared to the delayed intervention (control) practices where there was a small decline, from 5.56 (SD 1.54) to 5.27 (SD 1.62). The increase in ACIC scores was sustained one year after withdrawal of the PF intervention in the initial intervention group, from 6.33 (SD 1.50) to 6.60 (SD 1.94), and improved in the delayed intervention (control) practices during their one year of PF intervention, from 5.27 (SD 1.62) to 5.99 (SD 1.75).
Conclusions
Practice facilitation resulted in a significant and sustained improvement in delivery of care consistent with the CCM as reported by those involved in direct patient care in small primary care practices. The impact of the observed change on clinical outcomes remains uncertain.
Trial registration
This protocol followed the CONSORT guidelines and is registered per ICMJE guidelines: Clinical Trial Registration Number: NCT00482768.
doi:10.1186/1748-5908-8-93
PMCID: PMC3765887  PMID: 23965255
17.  A Qualitative Study of Rural Primary Care Clinician Views on Remote Monitoring Technologies 
Purpose
Remote monitoring technologies (RMTs) may improve quality of care, reduce access barriers, and help control medical costs. Despite the role of primary care clinicians as potential key users of RMTs, few studies explore their views. This study explores rural primary care clinician interest and the resources necessary to incorporate RMTs into routine practice.
Methods
We conducted 15 in-depth interviews with rural primary care clinician members of the Oregon Rural Practice-based Research Network (ORPRN) from November 2011 to April 2012. Our multidisciplinary team used thematic analysis to identify emergent themes and a cross-case comparative analysis to explore variation by participant and practice characteristics.
Results
Clinicians expressed interest in RMTs most relevant to their clinical practice, such as supporting chronic disease management, noting benefits to patients of all ages. They expressed concern about the quantity of data, patient motivation to utilize equipment, and potential changes to the patient-clinician encounter. Direct data transfer into the clinic’s electronic health record (EHR), availability in multiple formats, and review by ancillary staff could facilitate implementation. Although participants acknowledged the potential system-level benefits of using RMTs, adoption would be difficult without payment reform.
Conclusions
Adoption of RMTs by rural primary care clinicians may be influenced by equipment purpose and functionality, implementation resources, and payment. Clinician and staff engagement will be critical to actualize RMT use in routine primary care.
doi:10.1111/jrh.12027
PMCID: PMC3882331  PMID: 24383486
e-health; in-depth interview; primary care; qualitative research; rural health
18.  Developing a national dissemination plan for collaborative care for depression: QUERI Series 
Background
Little is known about effective strategies for disseminating and implementing complex clinical innovations across large healthcare systems. This paper describes processes undertaken and tools developed by the U.S. Department of Veterans Affairs (VA) Mental Health Quality Enhancement Research Initiative (MH-QUERI) to guide its efforts to partner with clinical leaders to prepare for national dissemination and implementation of collaborative care for depression.
Methods
An evidence-based quality improvement (EBQI) process was used to develop an initial set of goals to prepare the VA for national dissemination and implementation of collaborative care. The resulting product of the EBQI process is referred to herein as a "National Dissemination Plan" (NDP). EBQI participants included: a) researchers with expertise on the collaborative care model for depression, clinical quality improvement, and implementation science, and b) VA clinical and administrative leaders with experience and expertise on how to adapt research evidence to organizational needs, resources and capacity. Based on EBQI participant feedback, drafts of the NDP were revised and refined over multiple iterations before a final version was approved by MH-QUERI leadership. 'Action Teams' were created to address each goal. A formative evaluation framework and related tools were developed to document processes, monitor progress, and identify and act upon barriers and facilitators in addressing NDP goals.
Results
The National Dissemination Plan suggests that effectively disseminating collaborative care for depression in the VA will likely require attention to: Guidelines and Quality Indicators (4 goals), Training in Clinical Processes and Evidence-based Quality Improvement (6 goals), Marketing (7 goals), and Informatics Support (1 goal). Action Teams are using the NDP as a blueprint for developing infrastructure to support system-wide adoption and sustained implementation of collaborative care for depression. To date, accomplishments include but are not limited to: conduct of a systematic review of the literature to update VA depression treatment guidelines to include the latest evidence on collaborative care for depression; training for clinical staff on TIDES (Translating Initiatives for Depression into Effective Solutions project) care; spread of TIDES care to new VA facilities; and integration of TIDES depression assessment tools into a planned update of software used in delivery of VA mental health services. Thus far, common barriers encountered by Action Teams in addressing NDP goals include: a) limited time to address goals due to competing tasks/priorities, b) frequent turnover of key organizational leaders/stakeholders, c) limited skills and training among team members for addressing NDP goals, and d) difficulty coordinating activities across Action Teams on related goals.
Conclusion
MH-QUERI has partnered with VA organizational leaders to develop a focused yet flexible plan to address key factors to prepare for national dissemination and implementation of collaborative care for depression. Early indications suggest that the plan is laying an important foundation that will enhance the likelihood of successful implementation and spread across the VA healthcare system.
doi:10.1186/1748-5908-3-59
PMCID: PMC2631596  PMID: 19117524
19.  How to successfully select and implement electronic health records (EHR) in small ambulatory practice settings 
Background
Adoption of EHRs by U.S. ambulatory practices has been slow despite the perceived benefits of their use. Most evaluations of EHR implementations in the literature apply to large practice settings. While there are similarities relating to EHR implementation in large and small practice settings, the authors argue that scale is an important differentiator. Focusing on small ambulatory practices, this paper outlines the benefits and barriers to EHR use in this setting, and provides a "field guide" for these practices to facilitate successful EHR implementation.
Discussion
The benefits of EHRs in ambulatory practices include improved patient care and office efficiency, and potential financial benefits. Barriers to EHRs include costs; lack of standardization of EHR products and the design of vendor systems for large practice environments; resistance to change; initial difficulty of system use leading to productivity reduction; and perceived accrual of benefits to society and payers rather than providers. The authors stress the need for developing a flexible change management strategy when introducing EHRs that is relevant to the small practice environment; the strategy should acknowledge the importance of relationship management and the role of individual staff members in helping the entire staff to manage change. Practice staff must create an actionable vision outlining realistic goals for the implementation, and all staff must buy into the project. The authors detail the process of implementing EHRs through several stages: decision, selection, pre-implementation, implementation, and post-implementation. They stress the importance of identifying a champion to serve as an advocate of the value of EHRs and provide direction and encouragement for the project. Other key activities include assessing and redesigning workflow; understanding financial issues; conducting training that is well-timed and meets the needs of practice staff; and evaluating the implementation process.
Summary
The EHR implementation experience depends on a variety of factors including the technology, training, leadership, the change management process, and the individual character of each ambulatory practice environment. Sound processes must support both technical and personnel-related organizational components. Additional research is needed to further refine recommendations for the small physician practice and the nuances of specific medical specialties.
doi:10.1186/1472-6947-9-15
PMCID: PMC2662829  PMID: 19236705
20.  Effect of an Educational Toolkit on Quality of Care: A Pragmatic Cluster Randomized Trial 
PLoS Medicine  2014;11(2):e1001588.
In a pragmatic cluster-randomized trial, Baiju Shah and colleagues evaluated the effectiveness of printed educational materials for clinician education focusing on cardiovascular disease screening and risk reduction in people with diabetes.
Please see later in the article for the Editors' Summary
Background
Printed educational materials for clinician education are one of the most commonly used approaches for quality improvement. The objective of this pragmatic cluster randomized trial was to evaluate the effectiveness of an educational toolkit focusing on cardiovascular disease screening and risk reduction in people with diabetes.
Methods and Findings
All 933,789 people aged ≥40 years with diagnosed diabetes in Ontario, Canada were studied using population-level administrative databases, with additional clinical outcome data collected from a random sample of 1,592 high risk patients. Family practices were randomly assigned to receive the educational toolkit in June 2009 (intervention group) or May 2010 (control group). The primary outcome in the administrative data study, death or non-fatal myocardial infarction, occurred in 11,736 (2.5%) patients in the intervention group and 11,536 (2.5%) in the control group (p = 0.77). The primary outcome in the clinical data study, use of a statin, occurred in 700 (88.1%) patients in the intervention group and 725 (90.1%) in the control group (p = 0.26). Pre-specified secondary outcomes, including other clinical events, processes of care, and measures of risk factor control, were also not improved by the intervention. A limitation is the high baseline rate of statin prescribing in this population.
Conclusions
The educational toolkit did not improve quality of care or cardiovascular outcomes in a population with diabetes. Despite being relatively easy and inexpensive to implement, printed educational materials were not effective. The study highlights the need for a rigorous and scientifically based approach to the development, dissemination, and evaluation of quality improvement interventions.
Trial Registration
http://www.ClinicalTrials.gov NCT01411865 and NCT01026688
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Clinical practice guidelines help health care providers deliver the best care to patients by combining all the evidence on disease management into specific recommendations for care. However, the implementation of evidence-based guidelines is often far from perfect. Take the example of diabetes. This common chronic disease, which is characterized by high levels of sugar (glucose) in the blood, impairs the quality of life of patients and shortens life expectancy by increasing the risk of cardiovascular diseases (conditions that affect the heart and circulation) and other life-threatening conditions. Patients need complex care to manage the multiple risk factors (high blood sugar, high blood pressure, high levels of fat in the blood) that are associated with the long-term complications of diabetes, and they need to be regularly screened and treated for these complications. Clinical practice guidelines for diabetes provide recommendations on screening and diagnosis, drug treatment, and cardiovascular disease risk reduction, and on helping patients self-manage their disease. Unfortunately, the care delivered to patients with diabetes frequently fails to meet the standards laid down in these guidelines.
Why Was This Study Done?
How can guideline adherence and the quality of care provided to patients be improved? A common approach is to send printed educational materials to clinicians. For example, when the Canadian Diabetes Association (CDA) updated its clinical practice guidelines in 2008, it mailed educational toolkits that contained brochures and other printed materials targeting key themes from the guidelines to family physicians. In this pragmatic cluster randomized trial, the researchers investigate the effect of the CDA educational toolkit that targeted cardiovascular disease screening and treatment on the quality of care of people with diabetes. A pragmatic trial asks whether an intervention works under real-life conditions and whether it works in terms that matter to the patient; a cluster randomized trial randomly assigns groups of people to receive alternative interventions and compares outcomes in the differently treated “clusters.”
What Did the Researchers Do and Find?
The researchers randomly assigned family practices in Ontario, Canada to receive the educational toolkit in June 2009 (intervention group) or in May 2010 (control group). They examined outcomes between July 2009 and April 2010 in all patients with diabetes in Ontario aged over 40 years (933,789 people) using population-level administrative data. In Canada, administrative databases record the personal details of people registered with provincial health plans, information on hospital visits and prescriptions, and physician service claims for consultations, assessments, and diagnostic and therapeutic procedures. They also examined clinical outcome data from a random sample of 1,592 patients at high risk of cardiovascular complications. In the administrative data study, death or non-fatal heart attack (the primary outcome) occurred in about 11,500 patients in both the intervention and control group. In the clinical data study, the primary outcome―use of a statin to lower blood fat levels―occurred in about 700 patients in both study groups. Secondary outcomes, including other clinical events, processes of care, and measures of risk factor control were also not improved by the intervention. Indeed, in the administrative data study, some processes of care outcomes related to screening for heart disease were statistically significantly worse in the intervention group than in the control group, and in the clinical data study, fewer patients in the intervention group reached blood pressure targets than in the control group.
What Do These Findings Mean?
These findings suggest that the CDA cardiovascular diseases educational toolkit did not improve quality of care or cardiovascular outcomes in a population with diabetes. Indeed, the toolkit may have led to worsening in some secondary outcomes although, because numerous secondary outcomes were examined, this may be a chance finding. Limitations of the study include its length, which may have been too short to see an effect of the intervention on clinical outcomes, and the possibility of a ceiling effect—the control group in the clinical data study generally had good care, which left little room for improvement of the quality of care in the intervention group. Overall, however, these findings suggest that printed educational materials may not be an effective way to improve the quality of care for patients with diabetes and other complex conditions and highlight the need for a rigorous, scientific approach to the development, dissemination, and evaluation of quality improvement interventions.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001588.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health care professionals, and the general public (in English and Spanish)
The UK National Health Service Choices website provides information (including some personal stories) for patients and carers about type 2 diabetes, the commonest form of diabetes
The Canadian Diabetes Association also provides information about diabetes for patients (including some personal stories about living with diabetes) and health care professionals; its latest clinical practice guidelines are available on its website
The UK National Institute for Health and Care Excellence provides general information about clinical guidelines and about health care quality standards in the UK
The US Agency for Healthcare Research and Quality aims to improve the quality, safety, efficiency, and effectiveness of health care for all Americans (information in English and Spanish); the US National Guideline Clearinghouse is a searchable database of clinical practice guidelines
The International Diabetes Federation provides information about diabetes for patients and health care professionals, along with international statistics on the burden of diabetes
doi:10.1371/journal.pmed.1001588
PMCID: PMC3913553  PMID: 24505216
21.  Appreciative Inquiry for Quality Improvement in Primary Care Practices 
Purpose
To test the effect of an Appreciative Inquiry (AI) quality improvement strategy, on clinical quality management and practice development outcomes. AI enables discovery of shared motivations, envisioning a transformed future, and learning around implementation of a change process.
Methods
Thirty diverse primary care practices were randomly assigned to receive an AI-based intervention focused on a practice-chosen topic and on improving preventive service delivery (PSD) rates. Medical record review assessed change in PSD rates. Ethnographic fieldnotes and observational checklist analysis used editing and immersion/crystallization methods to identify factors affecting intervention implementation and practice development outcomes.
Results
PSD rates did not change. Field note analysis suggested that the intervention elicited core motivations, facilitated development of a shared vision, defined change objectives and fostered respectful interactions. Practices most likely to implement the intervention or develop new practice capacities exhibited one or more of the following: support from key leader(s), a sense of urgency for change, a mission focused on serving patients, health care system and practice flexibility, and a history of constructive practice change.
Conclusions
An AI approach and enabling practice conditions can lead to intervention implementation and practice development by connecting individual and practice strengths and motivations to the change objective.
doi:10.1097/QMH.0b013e31820311be
PMCID: PMC4222905  PMID: 21192206
22.  A quasi-experimental test of an intervention to increase the use of thiazide-based treatment regimens for people with hypertension 
Background
Despite recent high-quality evidence for their cost-effectiveness, thiazides are underused for controlling hypertension. The goal of this study was to design and test a practice-based intervention aimed at increasing the use of thiazide-based antihypertensive regimens.
Methods
This quasi-experimental study was carried out in general medicine ambulatory practices of a large, academically-affiliated Veterans Affairs hospital. The intervention group consisted of the practitioners (13 staff and 215 trainees), nurses, and patients (3,502) of the teaching practice; non-randomized concurrent controls were the practitioners (31 providers) and patients (18,292) of the non-teaching practices. Design of the implementation intervention was based on Rogers' Diffusion of Innovations model. Over 10.5 months, intervention teams met weekly or biweekly and developed and disseminated informational materials among themselves and to trainees, patients, and administrators. These teams also reviewed summary electronic-medical-record data on thiazide use and blood pressure (BP) goal attainment. Outcome measures were the proportion of hypertensive patients prescribed a thiazide-based regimen, and the proportion of hypertensive patients attaining BP goals regardless of regimen. Thirty-three months of time-series data were available; statistical process control charts, change point analyses, and before-after analyses were used to estimate the intervention's effects.
Results
Baseline use of thiazides and rates of BP control were higher in the intervention group than controls. During the intervention, thiazide use and BP control increased in both groups, but changes occurred earlier in the intervention group, and primary change points were observed only in the intervention group. Overall, the pre-post intervention difference in proportion of patients prescribed thiazides was greater in intervention patients (0.091 vs. 0.058; p = 0.0092), as was the proportion achieving BP goals (0.092 vs. 0.044; p = 0.0005). At the end of the implementation period, 41.4% of intervention patients were prescribed thiazides vs. 30.6% of controls (p < 0.001); 51.6% of intervention patients had achieved BP goals vs. 44.3% of controls (p < 0.001).
Conclusion
This multi-faceted intervention appears to have resulted in modest improvements in thiazide prescribing and BP control. The study also demonstrates the value of electronic medical records for implementation research, how Rogers' model can be used to design and launch an implementation strategy, and how all members of a clinical microsystem can be involved in an implementation effort.
doi:10.1186/1748-5908-2-5
PMCID: PMC1803001  PMID: 17298669
23.  Factors influencing performance of health workers in the management of seriously sick children at a Kenyan tertiary hospital - participatory action research 
Background
Implementation of World Health Organization case management guidelines for serious childhood illnesses remains a challenge in hospitals in low-income countries. Facilitators of and barriers to implementation of locally adapted clinical practice guidelines (CPGs) have not been explored.
Methods
This ethnographic study based on the theory of participatory action research (PAR) was conducted in Kenyatta National Hospital, Kenya’s largest teaching hospital. The primary intervention consisted of dissemination of locally adapted CPGs. The PRECEDE-PROCEED health education model was used as the conceptual framework to guide and examine further reinforcement activities to improve the uptake of the CPGs. Activities focussed on introduction of routine clinical audits and tailored educational sessions. Data were collected by a participant observer who also facilitated the PAR over an eighteen-month period. Naturalistic inquiry was utilized to obtain information from all hospital staff encountered while theoretical sampling allowed in-depth exploration of emerging issues. Data were analysed using interpretive description.
Results
Relevance of the CPGs to routine work and emergence of a champion of change facilitated uptake of best-practices. Mobilization of basic resources was relatively easily undertaken while activities that required real intellectual and professional engagement of the senior staff were a challenge. Accomplishments of the PAR were largely with the passive rather than active involvement of the hospital management. Barriers to implementation of best-practices included i) mismatch between the hospital’s vision and reality, ii) poor communication, iii) lack of objective mechanisms for monitoring and evaluating quality of clinical care, iv) limited capacity for planning strategic change, v) limited management skills to introduce and manage change, vi) hierarchical relationships, and vii) inadequate adaptation of the interventions to the local context.
Conclusions
Educational interventions, often regarded as ‘quick-fixes’ to improve care in low-income countries, may be necessary but are unlikely to be sufficient to deliver improved services. We propose that an understanding of organizational issues that influence the behaviour of individual health professionals should guide and inform the implementation of best-practices.
doi:10.1186/1472-6963-14-59
PMCID: PMC3942276  PMID: 24507629
Clinical audits; Clinical practice guidelines; Continuous medical educational sessions; ETAT+; Ethnographic study; Implementation of best-practices; Interpretive description; Participatory action research; Participant observer; Performance of health workers
24.  Pragmatic randomised controlled trial to evaluate guidelines for the management of infertility across the primary care-secondary care interface 
BMJ : British Medical Journal  2001;322(7297):1282.
Objective
To investigate the effect of clinical guidelines on the management of infertility across the primary care-secondary care interface.
Design
Cluster randomised controlled trial.
Setting
General practices and NHS hospitals accepting referrals for infertility in the Greater Glasgow Health Board area.
Participants
All 221 general practices in Glasgow; 214 completed the trial.
Intervention
General practices in the intervention arm received clinical guidelines developed locally. Control practices received them one year later. Dissemination of the guidelines included educational meetings.
Main outcome measures
The time from presentation to referral, investigations completed in general practice, the number and content of visits as a hospital outpatient, the time to reach a management plan, and costs for referrals from the two groups.
Results
Data on 689 referrals were collected. No significant difference was found in referral rates for infertility. Fewer than 1% of couples were referred inappropriately early. Referrals from intervention practices were significantly more likely to have all relevant investigations carried out (odds ratio 1.32, 95% confidence interval 1.00 to 1.75, P=0.025). 70% of measurements of serum progesterone concentrations during the midluteal phase and 34% of semen analyses were repeated at least once in hospital, despite having been recorded as normal when checked in general practice. No difference was found in the proportion of referrals in which a management plan was reached within one year or in the mean duration between first appointment and date of management plan. NHS costs were not significantly affected.
Conclusions
Dissemination of infertility guidelines by commonly used methods results in a modest increase in referrals having recommended investigations completed in general practice, but there are no detectable differences in outcome for patients or reduction in costs. Clinicians in secondary care tended to fail to respond to changes in referral practice by doctors. Guidelines that aim to improve the referral process need to be disseminated and implemented so as to lead to changes in both primary care and secondary care.
What is already known on this topicMost previous research into clinical guidelines has focused on their development and implementationEvidence is lacking about the outcomes and costs associated with the use of clinical guidelinesWhat this study addsClinical guidelines that may alter the balance of care between general practice and hospital settings require more intensive implementation than guidelines aimed at either setting on its ownThe cost effectiveness of clinical guidelines should not be assumed
PMCID: PMC31924  PMID: 11375232
25.  Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain 
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.
doi:10.1186/1748-5908-5-26
PMCID: PMC2868045  PMID: 20385018

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