title: Effect of medical records' checklists on implementation of periodic health measures. Clinical effect of computer generated physician reminders in health screening in primary health care - a controlled clinical trial of preventive services among the elderly Electronic Alerts Versus On-Demand Decision Support to Improve Dyslipidemia Treatment: A Cluster Randomized Controlled Trial Improving chronic disease prevention and screening in primary care: results of the BETTER pragmatic cluster randomized controlled trial A real-world stepped wedge cluster randomized trial of practice facilitation to improve cardiovascular care A randomized outpatient trial of a decision-support information technology tool. Promotion of health in older people: a randomised controlled trial of health risk appraisal in British general practice. Impact of an informed choice invitation on uptake of screening for diabetes in primary care (DICISION): randomised trial Improving attendance for cardiovascular risk assessment in Australian general practice: an RCT of a monetary incentive for patients Evidence to action: a tailored multifaceted approach to changing family physician practice patterns and improving preventive care. Association of cardiovascular health screening with mortality, clinical outcomes, and health care cost: a nationwide cohort study. Using nurses for preventive activities with computer assisted follow up: a randomised controlled trial. Automated electronic reminders to facilitate primary cardiovascular disease prevention: randomised controlled trial. Patients and computers as reminders to screen for diabetes in family practice Quantitative Synthesis in Systematic Reviews Increasing pneumococcal vaccination in managed care through telephone outreach. Improving prevention systems in primary care practices: the Health Education and Research Trial (HEART) Computer-generated physician and patient reminders. Tools to improve population adherence to selected preventive services. Participation in health check-ups and mortality using propensity score matched cohort analyses. Opportunistic electronic reminders. Improving performance of preventive care in general practice. The impact of office cholesterol testing. A Randomized Trial of Computerized Reminders for Blood Pressure Screening in Primary Care
abstract: Recent re-evaluation of preventive health care has result ed in more limited and directed guidelines ; nonetheless , physician compliance has remained poor . This study assessed whether an inexpensive reminder system of preventive care checklists would improve physician implementation of periodic health measures .  Residents in internal medicine were r and omly placed into two groups : one received a copy of the appropriate checklist with each patient 's medical record ; the other did not . After one year , 200 r and omly selected records were audited to determine the proportion of recommendations implemented for each patient . Residents who received checklists performed appropriate preventive health measures at a significantly higher rate than those who did not ( 0.56 + /- 0.26 versus 0.39 + /- 0.22 , p less than 0.002 ) . The actual use of the checklist to record the results was associated with an even higher rate of compliance compared with instances in which the checklists were provided but not used and instances in which checklists were not received ( 0.70 + /- 0.21 versus 0.44 + /- 0.24 and 0.39 + /- 0.22 , respectively , p less than 0.002 ) . These data suggest that a physician 's use of simple checklists can provide an inexpensive and effective means of improving implementation of periodic health maintenance OBJECTIVES To apply and evaluate the effects of a program for computer generated physician reminders , integrated with an electronic patient record ( EPR ) system , for opportunistic health screening in elderly patients . DESIGN A pilot study design ed as a 20-month clinical trial with a control group and a 20-month non-intervention follow-up using a computer reminder program that selects patients for screening in five intervention areas ( diabetes , hypertension , cobalamin deficiency , hypothyroidism and anaemia ) . SETTING Four primary health care ( PHC ) centres in suburban Stockholm . SUBJECTS The intervention was design ed for patients 70 years or older from one health care centre who visited a general practitioner ( GP ) during the first 20-month period . Patients from the three remaining centres served as controls . MAIN OUTCOME MEASURES The number of patients who underwent the tests , who had pathological test results , new diagnoses and new pharmacological treatments in both patient groups . RESULTS In total , 602 patients underwent screening and 1989 were controls . There was a statistically significant , moderate or marked increase ( 13 - 75 % ) in the number of patients who were tested in all five intervention areas . An increase in pathological test results ( 1 - 8 % ) was found in two areas : hypertension and cobalamin deficiency . There was an increase in the number of patients with the diagnosis of cobalamin deficiency during the study . At follow-up a decrease in new diagnoses for anaemia was found . CONCLUSION The system seems to be associated with a moderate to large increase in laboratory and manual screening tests for both established and new screening areas . The effect on clinical outcomes was found mainly in a not-yet-established screening area ( cobalamin deficiency ) , indicating that the system may be clinical ly useful when introducing new screening services Background — Indirect evidence shows that alerting users with clinical decision support systems seems to change behavior more than requiring users to actively initiate the system . However , r and omized trials comparing these methods in a clinical setting are lacking . We studied the effect of both alerting and on-dem and decision support with respect to screening and treatment of dyslipidemia based on the guidelines of the Dutch College of General Practitioners . Methods and Results — In a clustered r and omized trial design , 38 Dutch general practice s ( 77 physicians ) and 87 886 of their patients ( 39 433 men 18 to 70 years of age and 48 453 women 18 to 75 years of age ) who used the ELIAS electronic health record participated . Each practice was assigned to receive alerts , on-dem and support , or no intervention . We measured the percentage of patients screened and treated after 12 months of follow-up . In the alerting group , 65 % of the patients requiring screening were screened ( relative risk versus control=1.76 ; 95 % confidence interval , 1.41 to 2.20 ) compared with 35 % of patients in the on-dem and group ( relative risk versus control=1.28 ; 95 % confidence interval , 0.98 to 1.68 ) and 25 % of patients in the control group . In the alerting group , 66 % of patients requiring treatment were treated ( relative risk versus control=1.40 ; 95 % confidence interval , 1.15 to 1.70 ) compared with 40 % of patients ( relative risk versus control=1.19 ; 95 % confidence interval , 0.94 to 1.50 ) in the on-dem and group and 36 % of patients in the control group . Conclusion — The alerting version of the clinical decision support systems significantly improved screening and treatment performance for dyslipidemia by general practitioners Background Primary care provides most of the evidence -based chronic disease prevention and screening services offered by the healthcare system . However , there remains a gap between recommended preventive services and actual practice . This trial ( the BETTER Trial ) aim ed to improve preventive care of heart disease , diabetes , colorectal , breast and cervical cancers , and relevant lifestyle factors through a practice facilitation intervention set in primary care . Methods Pragmatic two-way factorial cluster RCT with Primary Care Physicians ’ practice s as the unit of allocation and individual patients as the unit of analysis . The setting was urban Primary Care Team practice s in two Canadian provinces . Eight Primary Care Team practice s were r and omly assigned to receive the practice -level intervention or wait-list control ;  4 physicians in each team ( 32 physicians ) were r and omly assigned to receive the patient-level intervention or wait-list control .  Patients r and omly selected from physicians ’ rosters were stratified into two groups : 1 ) general and 2 ) moderate mental illness . The interventions involved a multifaceted , evidence -based , tailored practice -level intervention with a Practice Facilitator , and a patient-level intervention involving a one-hour visit with a Prevention Practitioner where patients received a tailored ‘ prevention prescription ’ . The primary outcome was a composite Summary Quality Index of 28 evidence -based chronic disease prevention and screening actions with pre-defined targets , expressed as the ratio of eligible actions at baseline that were met at follow-up . A cost-effectiveness analysis was conducted . Results 789 of 1,260 ( 63 % ) eligible patients participated . On average , patients were eligible for 8.96 ( SD 3.2 ) actions at baseline . In the adjusted analysis , control patients met 23.1 % ( 95 % CI : 19.2 % to 27.1 % ) of target actions , compared to 28.5 % ( 95 % CI : 20.9 % to 36.0 % ) receiving the practice -level intervention , 55.6 % ( 95 % CI : 49.0 % to 62.1 % ) receiving the patient-level intervention , and 58.9 % ( 95 % CI : 54.7 % to 63.1 % ) receiving both practice - and patient-level interventions ( patient-level intervention versus control , P < 0.001 ) . The benefit of the patient-level intervention was seen in both strata . The extra cost of the intervention was $ 26.43CAN ( 95 % CI : $ 16 to $ 44 ) per additional action met . Conclusions A Prevention Practitioner can improve the implementation of clinical ly important prevention and screening for chronic diseases in a cost-effective manner Background Practice facilitation has been associated with meaningful improvements in disease prevention and quality of patient care . Using practice facilitation , the Improved Delivery of Cardiovascular Care ( IDOCC ) project aim ed to improve the delivery of evidence -based cardiovascular care in primary care practice s across a large health region . Our goal was to evaluate IDOCC ’s impact on adherence to processes of care delivery . Methods A pragmatic stepped wedge cluster r and omized trial recruiting primary care providers in practice s located in Eastern Ontario , Canada ( Clinical Trials.gov : NCT00574808 ) . Participants were r and omly assigned by region to one of three steps . Practice facilitators were intended to visit practice s every 3–4 ( year 1—intensive ) or 6–12 weeks ( year 2—sustainability ) to support changes in practice behavior . The primary outcome was mean adherence to indicators of evidence -based care measured at the patient level . Adherence was assessed by chart review of a r and omly selected cohort of 66 patients per practice in each pre-intervention year , as well as in year 1 and year 2 post-intervention . Results Eighty-four practice s ( 182 physicians ) participated . On average , facilitators had 6.6 ( min : 2 , max : 11 ) face-to-face visits with practice s in year 1 and 2.5 ( min : 0 max : 10 ) visits in year 2 . We collected chart data from 5292 patients . After adjustment for patient and provider characteristics , there was a 1.9 % ( 95 % confidence interval ( CI ) : −2.9 to −0.9 % ) and 4.2 % ( 95 % CI : −5.7 to −2.6 % ) absolute decrease in mean adherence from baseline to intensive and sustainability years , respectively . Conclusions IDOCC did not improve adherence to best- practice guidelines . Our results showed a small statistically significant decrease in mean adherence of question able clinical significance . Potential reasons for this result include implementation challenges , competing priorities in practice s , a broad focus on multiple chronic disease indicators , and use of an overall index of adherence . These results contrast with findings from previously reported facilitation trials and highlight the complexities and challenges of translating research findings into clinical practice .Trial registration Clinical Trials.gov BACKGROUND Decision-support information technology is often adopted to improve clinical decision making , but it is rarely rigorously evaluated . Congress m and ated the evaluation of Problem-Knowledge Couplers ( PKC Corp , Burlington , Vt ) , a decision-support tool proposed for the Department of Defense 's new health information network . METHODS This was a patient-level r and omized trial conducted at 2 military practice s. A total of 936 patients were allocated to the intervention group  and 966 to usual care .  Couplers were applied before routine ambulatory clinic visits . The primary outcome was quality of care , which was assessed based on the total percentage of any of 24 health care quality process measures ( opportunities to provide evidence -based care ) that were fulfilled . Secondary outcomes included medical re sources consumed within 60 days of enrollment and patient and provider satisfaction . RESULTS There were 4639 health care opportunities ( 2374 in the Coupler group and 2265 in the usual-care group ) , with no difference in the proportion of opportunities fulfilled ( 33.9 % vs 30.7 % ; P = .12 ) . Although there was a modest improvement in performance on screening/preventive measures , it was offset by poorer performance on some measures of acute care . Coupler patients used more laboratory and pharmacy re sources than usual-care patients ( logarithmic mean difference , 71 dollars ) . No difference in patient satisfaction was observed between groups , and provider satisfaction was mixed . CONCLUSION This study provides no strong evidence to support the utility of this decision-support tool , but it demonstrates the value of rigorous evaluation of decision-support information technology BACKGROUND there is inadequate evidence to support currently formulated NHS strategies to achieve health promotion and preventative care in older people through broad-based screening and assessment in primary care . The most extensively evaluated delivery instrument for this purpose is Health Risk Appraisal ( HRA ) . This article describes a trial using HRA to evaluate the effect on health behaviour and preventative-care uptake in older people in NHS primary care . METHODS a r and omised controlled trial was undertaken in three London primary care group practice s. Functionally independent community-dwelling patients older than 65 years ( n = 2,503 ) received a self-administered Health Risk Appraisal for Older Persons ( HRA-O ) question naire leading to computer-generated individualised written feedback to participants and general practitioners ( GPs ) , integrated into practice information-technology ( IT ) systems . All primary care staff received training in preventative health in older people . The main outcome measures were self-reported health behaviour and preventative care uptake at 1-year follow-up . RESULTS of 2,503 individuals r and omised , 2,006 respondents ( 80.1 % ) ( intervention , n = 940 , control n = 1,066 ) were available for analysis . Intervention group respondents reported slightly higher pneumococcal vaccination uptake and equivocal improvement in physical activity levels compared with controls . No significant differences were observed for any other categories of health behaviour or preventative care measures at 1-year follow-up . CONCLUSIONS HRA-O implemented in this way result ed in minimal improvement of health behaviour or uptake of preventative care measures in older people . Supplementary reinforcement involving contact by health professionals with patients over and above routine clinical encounters may be a prerequisite to the effectiveness of IT-based delivery systems for health promotion in older people Objective To compare the effect of an invitation promoting informed choice for screening with a st and ard invitation on attendance and motivation to engage in preventive action . Design R and omised controlled trial . Setting Four English general practice s. Participants 1272 people aged 40 - 69 years , at risk for diabetes , identified from practice registers using a vali date d risk score and invited to attend for screening . Intervention Intervention was a previously vali date d invitation to inform the decision to attend screening , presenting diabetes as a serious potential problem , and providing details of possible costs and benefits of screening and treatment in text and pie charts . This was compared with a brief , st and ard invitation simply describing diabetes as a serious potential problem . Main outcome measures The primary end point was attendance for screening . The secondary outcome measures were intention to make changes to lifestyle and satisfaction with decisions made among attenders . Results The primary end point was analysed for all 1272 participants . 55.8 % ( 353/633 ) of those in the informed choice group attended for screening , compared with 57.6 % ( 368/639 ) in the st and ard invitation group ( mean difference −1.8 % , 95 % confidence interval −7.3 % to 3.6 % ; P=0.51 ) . Attendance was lower among the more deprived group ( most deprived third 47.5 % v least deprived third 64.3 % ; P<0.001 ) . Interaction between deprivation and effect of invitation type on attendance was not significant . Among attenders , intention to change behaviour was strong and unaffected by invitation type . Conclusions Providing information to support choice did not adversely affect attendance for screening for diabetes . Those from more socially deprived groups were , however , less likely to attend , regardless of the type of invitation received . Further attention to invitation content alone is unlikely to achieve equity in uptake of preventive services . Trial registration Current Controlled Trials IS RCT N 73125647 Background Preventive health care is an important part of general practice however uptake of activities by patients is variable . Monetary incentives for doctors have been used in the UK and Australia to improve rates of screening and immunisation . Few studies have focussed on incentives for patients to attend preventive health care examinations . Our objective was to investigate the use of a monetary incentive to increase patient attendance with their general practitioner for a cardiovascular risk assessment ( CVRA ) . Methods A pragmatic RCT was conducted in two Australian general practice s. Participating GPs underwent academic detailing for cardiovascular risk assessment .  301 patients aged 40–74 , who did not have cardiovascular disease , were independently r and omised to receive a letter inviting them to a no cost cardiovascular risk assessment with their GP , or the same letter plus an offer of a $ 25 shopping voucher if they attended . An audit of patient medical records was also undertaken and a patient question naire administered to a sub sample of participants . Our main outcome measure was attendance for cardiovascular risk assessment . Results In the RCT , 56/301(18.6 % ) patients attended for cardiovascular risk assessment , 29/182 ( 15.9 % ) in the control group and 27/119 ( 22.7 % ) in the intervention group . The estimated difference of 6.8 % ( 95 % CI : -2.5 % to 16.0 % ) was not statistically significant , P = 0.15 . The audit showed that GPs may underestimate patients ’ absolute cardiovascular risk and the question naire that mailed invitations from GPs for a CVRA may encourage patients to attend . Conclusions A small monetary incentive  does not improve attendance for cardiovascular risk assessment . Further research should be undertaken to determine if there are other incentives that may increase attendance for preventive activities in the general practice setting . Clinical trials registration BACKGROUND Although there is much room for improvement in the performance of recommended preventive manoeuvres , many inappropriate preventive interventions are being done . We evaluated a multifaceted intervention , delivered by nurses trained in prevention facilitation , to improve prevention in primary care . METHODS Forty-six health service organizations ( HSOs ) were recruited from 100 sites in Ontario . After baseline data were collected , we r and omly assigned the practice s to either an 18-month ( July 1997 to December 1998 ) multifaceted intervention delivered by 1 of 3 nurse facilitators ( 23 practice s ) or no intervention ( 23 practice s ) . The unit of intervention and analysis was the medical practice . The outcome measure was an overall index of preventive performance , which was calculated as the proportion of eligible patients who received 8 recommended preventive manoeuvres less the proportion of eligible patients who received 5 inappropriate preventive manoeuvres . RESULTS One HSO , in the intervention group , was lost to follow-up . Before the intervention , the index of preventive performance was similar for the intervention and control groups ( 31.9 % [ 95 % confidence interval ( CI ) 27.3%-36.5 % ] and 32.1 % [ 95 % CI 27.2%-37.0 % ] respectively ) . At follow-up the corresponding values were 43.2 % ( 95 % CI 38.4%-48.0 % ) and 31.9 % ( 95 % CI 26.8%-37.0 % ) , for an absolute improvement in the intervention group of 11.5 % ( p < 0.001 ) . The mean proportion of eligible patients who received the recommended manoeuvres was 62.3 % ( 95 % CI 58.2%-66.4 % ) in the intervention group , as compared with 57.4 % ( 95 % CI 54.1%-60.7 % ) in the control group , for an absolute improvement of 7.2 % ( p = 0.008 ) . The corresponding values for the inappropriate manoeuvres were 19.1 % ( 95 % CI 15.6%-22.6 % ) and 25.5 % ( 95 % CI 20.0%-31.0 % ) , for an absolute improvement of 4.4 % ( p = 0.019 ) . INTERPRETATION The tailored multifaceted intervention delivered by nurse facilitators was effective in modifying physician practice patterns and significantly improved preventive care performance OBJECTIVE To determine whether a  cardiovascular disease ( CVD ) health screening program is associated with CVD-related health conditions , incidence of cardiovascular events , mortality , healthcare utilization , and costs . METHODS Cohort study of a 3 % r and om sample of all Korea National Health Insurance members 40years of age or older and free of CVD or CVD-related health conditions was conducted . A total 443,337 study participants were followed-up from January 1 , 2005 through December 31 , 2010 . RESULTS In primary analysis , the hazard ratios for CVD mortality , all-cause mortality , incident composite CVD events , myocardial infa rct ion , cerebral infa rct ion , and cerebral hemorrhage comparing participants who attended a screening exam during 2003 - 2004 compared to those who did not were 0.58 ( 95 % CI : 0.53 - 0.63 ) , 0.62 ( 95 % CI : 0.60 - 0.64 ) , 0.82 ( 95 % CI : 0.78 - 0.85 ) , 0.84 ( 95 % CI : 0.75 - 0.93 ) , 0.84 ( 95 % CI : 0.79 - 0.89 ) , and 0.73 ( 95 % CI : 0.67 - 0.80 ) , respectively . Screening attenders had higher rates of newly diagnosed hypertension , diabetes mellitus , and dyslipidemia , lower inpatient days of stay and cost , and lower outpatient cost compared to non-attenders . CONCLUSIONS Participation in CVD health screening was associated with lower rates of CVD , all-cause mortality , and CVD events , higher detection of CVD-related health conditions , and lower healthcare utilization and costs OBJECTIVE --To assess whether an organised programme of prevention including the use of a health promotion nurse noticeably improved recording and follow up of cardiovascular risk factors and cervical smears in a general practice that had access to computerised cell and recall . DESIGN --R and omised controlled trial . SETTING --General practice in inner London . PATIENTS --All 3206 men and women aged 30 - 64 registered with the practice . INTERVENTION -- The intervention group had their risk factors ascertained and followed up by the health promotion nurse and the general practitioner , whereas those in the control group were managed by the general practitioner alone . END POINT -- Recording and follow up of blood pressure and cervical smears after three years . Recording of smoking , family history of ischaemic heart disease , and serum cholesterol concentrations were also examined . MEASUREMENTS and MAIN RESULTS --When the trial was stopped after two years the measurements of blood pressure in the preceding five years were 93 % ( 1511/1620 ) v 73 % ( 1160/1586 ) ( 95 % confidence interval for difference 17.5 to 22.7 % ) for intervention and control groups respectively . For patients with hypertension the figures were 97 % ( 104/107 ) v 69 % ( 80/116 ) ( 18.2 to 38.2 % ) . For women the proportion who had had a cervical smear in the preceding three years were 76 % ( 606/799 ) v 49 % ( 392/806 ) ( 22.5 to 31.9 % ) . Recording of smoking , family history of ischaemic heart disease , and serum cholesterol concentrations was also higher in the intervention group compared with the control group . CONCLUSION --An organised programme , which includes a nurse with specific responsibility for adult prevention , is likely to make an important contribution to recording of risk factors and follow up of those patients with known risks BACKGROUND Primary care data bases contain cardiovascular disease risk factor data , but practical tools are required to improve identification of at-risk patients . AIM To test the effects of a system of electronic reminders ( the ' e-Nudge ' ) on cardiovascular events and the adequacy of data for cardiovascular risk estimation . DESIGN OF STUDY R and omised controlled trial . SETTING Nineteen general practice s in the West Midl and s , UK . METHOD The e-Nudge identifies four groups of patients aged over 50 years on the basis of estimated cardiovascular risk and adequacy of risk factor data in general practice computers . Screen messages highlight individuals at raised risk and prompt users to complete risk profiles where necessary . The proportion of the study population in the four groups was measured , as well as the rate of cardiovascular events in each arm after 2 years . RESULTS Over 38 000 patients ' electronic records were r and omised . The intervention led to an increase in the proportion of patients with sufficient data who were identifiably at risk , with a difference of 1.94 % compared to the control group ( 95 % confidence interval [ CI ] = 1.38 to 2.50 , P<0.001 ) . A corresponding reduction occurred in the proportion potentially at risk but requiring further data for a risk estimation ( difference = -3.68 % , 95 % CI = -4.53 to -2.84 , P<0.001 ) . No significant difference was observed in the incidence of cardiovascular events ( rate ratio = 0.96 , 95 % CI = 0.85 to 1.10 , P = 0.59 ) . CONCLUSION Automated electronic reminders using routinely collected primary care data can improve the adequacy of cardiovascular risk factor information during everyday practice and increase the visibility of the at-risk population Abstract BACKGROUND : In New Zeal and , more than 5 % of people aged 50 years and older have undiagnosed diabetes ; most of them attend family practitioners ( FPs ) at least once a year . OBJECTIVES : To test the effectiveness of patients or computers as reminders to screen for diabetes in patients attending FPs . DESIGN : A r and omized-controlled trial compared screening rates in 4 intervention arms : patient reminders , computer reminders , both reminders , and usual care . The trial lasted 2 months . The patient reminder was a diabetes risk self- assessment sheet filled in by patients and given to the FP during the consultation . The computer reminder was an icon that flashed only for patients considered eligible for screening . PARTICIPANTS : One hundred and seven FPs . MEASUREMENTS : The primary outcome was whether each eligible patient , who attended during the trial , was or was not tested for blood glucose . Analysis was by intention to treat and allowed for clustering by FP . RESULTS : Patient reminders ( odds ratio [ OR ] 1.72 , 95 % confidence interval [ CI ] 1.21 , 2.43 ) , computer reminders ( OR 2.55 , 1.68 , 3.88 ) , and both reminders ( OR 1.69 , 1.11 , 2.59 ) were all effective compared with usual care . Computer reminders were more effective than patient reminders ( OR 1.49 , 1.07 , 2.07 ) . Patients were more likely to be screened if they visited the FP repeatedly , if patients were non-European , if they were “ regular ” patients of the practice , and if their FP had a higher screening rate prior to the study . CONCLUSIONS : Patient and computer reminders were effective methods to increase screening for diabetes . However , the effects were not additive A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment effect offer complementary information , and all should be examined [ 4 ] . Continuous data can be summarized by the raw mean difference between the treatment and control groups when the treatment effect is measured on the same scale ( for example , diastolic blood pressure in mm Hg ) , by the st and ardized mean difference when different scales are used to measure the same treatment effect ( for example , different pain scales being combined ) , or by the correlation coefficients between two continuous variables [ 5 ] . The st and ardized mean difference , also called the effect size , is obtained by dividing the difference between the mean in the treatment group and the mean in the control group by the SD in the control group . Evaluating the Statistical Heterogeneity of the Data This step is intended to answer the question , Are the results of the different studies similar ( homogeneous ) ? It is important to answer this question before combining any data . To do this , one must calculate the magnitude of the statistical diversity ( heterogeneity ) of the treatment effect that exists among the different sets of data . Statistical diversity can be thought of as attributable to one or both of two causes . First , study results can differ because of r and om sampling error . Even if the true effect is the same in each study , the results of different studies would be expected to vary r and omly around the true common fixed effect . This diversity is called the within- study variance . Second , each study may have been drawn from a different population , depending on the particular patients chosen and the interventions and conditions unique to the study . Therefore , even if each study enrolled a large patient sample , the treatment effect would be expected to differ . These differences , called r and om effects , describe the between- study variation with regard to an overall mean of the effects of all of the studies that could be undertaken . The test most commonly used to assess the statistical significance of between- study heterogeneity is based on the chi-square distribution [ 6 ] . It provides a measure of the sum of the squared differences between the results observed and the results expected in each study , under the assumption that each study estimates the same common treatment effect . A large total deviation indicates that a single common treatment effect is unlikely . Any pooled estimate calculated must account for the between- study heterogeneity . In practice , this test has low sensitivity for detecting heterogeneity , and it has been suggested that a liberal significance level , such as 0.1 , should be used [ 6 ] . Estimating a Common Effect The questions that this step tries to answers are , 1 ) To the extent that data are similar , what is their best common point estimate of a therapeutic effect , and 2 ) how precise is this estimate ? The mathematical process involved in this step generally involves combining ( pooling ) the results of different studies into an overall estimate . Compared with the results of individual studies , pooled results can increase statistical power and lead to more precise estimates of treatment effect . Each study is given a weight according to the precision of its results . The rationale is that studies with narrow CIs should be weighted more heavily than studies with greater uncertainty . The precision is generally expressed by the inverse of the variance of the estimate of each study . The variance has two components : the variance of the individual study and the variance between different studies . When the between- study variance is found to be or assumed to be zero , each study is simply weighted by the inverse of its own variance , which is a function of the study size and the number of events in the study . This approach characterizes a fixed-effects model , as exemplified by the Mantel-Haenszel method [ 7 , 8 ] or the Peto method [ 9 ] for dichotomous data . The Peto method has been particularly popular in the past . It has the advantage of simple calculation ; however , although it is appropriate in most cases , it may introduce large biases if the data are unbalanced [ 10 , 11 ] . On the other h and , r and om-effects models also add the between- study variance to the within- study variance of each individual study when the pooled mean of the r and om effects is calculated . The r and om-effects model most commonly used for dichotomous data is the DerSimonian and Laird estimate of the between- study variance [ 12 ] . Fixed- and r and om-effects models for continuous data have also been described [ 13 ] . Pooled results are generally reported as a point estimate and CI , typically a 95 % CI . Other quantitative techniques for combining data , such as the Confidence Profile Method [ 14 ] , use Bayesian methods to calculate posterior probability distributions for effects of interest . Bayesian statistics are based on the principle that each observation or set of observations should be viewed in conjunction with a prior probability describing the prior knowledge about the phenomenon of interest [ 15 ] . The new observations alter this prior probability to generate a posterior probability . Traditional meta- analysis assumes that nothing is known about the magnitude of the treatment effect before r and omized trials are performed . In Bayesian terms , the prior probability distribution is noninformative . Bayesian approaches may also allow the incorporation of indirect evidence in generating prior distributions [ 14 ] and may be particularly helpful in situations in which few data from r and omized studies exist [ 16 ] . Bayesian analyses may also be used to account for the uncertainty introduced by estimating the between- study variance in the r and om-effects model , leading to more appropriate estimates and predictions of treatment efficacy [ 17 ] . Exploring and Explaining Heterogeneity The next important issue is whether the common estimate obtained in the previous step is robust . Sensitivity analyses determine whether the common estimate is influenced by changes in the assumptions and in the protocol for combining the data . A comparison of the results of fixed- and r and om-effects models is one such sensitivity analysis [ 18 ] . Generally , the r and om-effects model produces wider CIs than does the fixed-effects model , and the level of statistical significance may therefore be different depending on the model used . The pooled point estimate per se is less likely to be affected , although exceptions are possible [ 19 ] . Other sensitivity analyses may include the examination of the residuals and the chi-square components [ 13 ] and assessment of the effect of deleting each study in turn . Statistically significant results that depend on a single study may require further exploration . Cumulative Meta- Analysis OBJECTIVES To determine the effectiveness of a telephone reminder to increase pneumococcal vaccination in a population that had received mailed reminders and to evaluate whether the intervention effect is similar for clinics serving primarily non-Hispanic black or non-Hispanic white patient population s. STUDY DESIGN R and omized trial within a managed care network . METHODS All unvaccinated patients 18 years and older with chronic medical conditions and 65 years and older without chronic medical conditions ( N = 6106 ) were r and omized to receive telephone intervention or st and ard care and followed up for 6-month vaccination status . The intervention was a telephone call initiated by a nurse to inform patients that pneumococcal vaccination was recommended and was a covered benefit of their insurance . RESULTS Intervention patients were 2.3 times as likely to be vaccinated during the study period than control patients ( P < .001 ) . The success of telephone intervention versus control was similar across clinics ( P = .16 ) and across the chronic disease and elderly groups ( P = .14 ) . In subanalyses of individuals reached by telephone intervention , unvaccinated black subjects were less likely to be vaccinated during the study than unvaccinated white subjects ( 34 % vs 25 % , P = .03 ) . Nurse staff time for telephone intervention cost $ 147.35 per additional patient vaccinated . CONCLUSIONS Telephone intervention was successful at increasing vaccination rates in a diverse managed care population that had already received mailed reminders . Tailored messaging for pneumococcal vaccination through telephone reminders increases patient dem and for vaccination and should be implemented by managed care organizations seeking to increase their vaccination rates BACKGROUND The Health Education and Research Trial ( HEART ) was a multicenter clinical trial design ed to test methods to improve primary care practice systems for heart disease prevention services . We present the trial methodology , the practice s ' use of medical record tools , and changes in documentation of cardiovascular risk factor screening and management . METHODS Primary care practice s were recruited from 4 Midwestern states . The factorial design result ed in 4 study groups : conference only , conference and quality improvement consultations , conference and prevention coordinator , and all interventions combined .  Medical record audits and physician , staff , and patient surveys assessed practice change in cardiovascular disease risk factor documentation . RESULTS Practice s participated fully in this project , set goals to improve preventive services , and implemented recommended medical record tools . The number of goals set and the increase in the use of medical record tools were greatest in the combined intervention group , with improvements noted in all groups . The use of patient history question naires , problem lists , and flow sheets was significantly higher in the combined intervention group when compared with the conference-only group . Documentation of risk factor screening in a recommended-medical record location improved in all intervention groups , with significant sustained improvements in the practice s that received the combined intervention . Documented risk factor management significantly improved in all intervention groups compared with the conference-only control . CONCLUSION Primary care practice s are interested in improving prevention systems and can change these systems in response to supportive external interventions . Promoting organizational change to produce sustained improvement in preventive service clinical outcomes is a complex process that requires further research Despite an emerging consensus on appropriate preventive services , a minority of patients receive them . A study was undertaken to assess the impact of computer-generated reminders to adult patients , their physicians , or both patients and physicians on adherence to five recommended preventive services : cholesterol measurements , fecal occult blood testing , mammography , Papanicolaou smears , and tetanus immunization . During the academic year 1988 - 1989 , all 7397 adult patients and their 49 physicians in a university family medicine clinical practice were r and omized by practice group into one of four study groups : control , physician reminders , patient reminders , and both physician and patient reminders . Adherence was defined in community-oriented terms : the percentage of patients within each group who had received the preventive service in the recommended interval . During the study period , adherence to four of the five preventive services increased significantly , with the largest increases in the physician and patient reminder group : cholesterol measurements increased from 19.5 % to 38.1 % , fecal occult blood testing 9.3 % to 27.0 % , mammography 11.4 % to 27.1 % , and tetanus immunization 23.4 % to 35.4 % ( for each increase , P less than .0001 , McNemar 's chi-square test ) . In general , increases were greater in blacks and in patients with any form of insurance coverage .  Computer-based physician and patient reminder systems have great promise of improving adherence to preventive services in primary care setting OBJECTIVE All Japanese aged ≥40 years are eligible for free annual health check-ups including blood pressure and cholesterol measurements . It is well known that health check-up screenees are more likely to have healthy lifestyles and better health conditions than non-screenees . Therefore , controlling these factors is required to investigate whether screenees have a lower mortality risk than non-screenees independent of their lifestyles or health conditions . METHODS We followed 48,775 Japanese National Health Insurance beneficiaries aged 40 - 79 years since 1994 for 11 years . We used Cox proportional hazard models adjusted for possible confounding factors . We also performed propensity for use of the health check-up matched cohort analyses . RESULTS Compared to non-screenees , multiple-adjusted hazard ratios ( 95 % confidence intervals ) for all-cause and cardiovascular disease mortality among screenees were 0.74 ( 0.62 - 0.88 ) and 0.65 ( 0.44 - 0.95 ) for men and 0.69 ( 0.52 - 0.91 ) and 0.61 ( 0.36 - 1.04 ) for women , respectively . These relations were also observed when we used propensity matched cohort analyses . CONCLUSION This is the first study to show that mortality rates are lower among screenees than non-screenees in Japanese health check-ups when propensity matched cohort analyses were used for adjusting confounding factors . Further prospect i ve studies , including r and omized controlled trials , are required to confirm whether screening lowers mortality BACKGROUND Preventive care is an important role for general practitioners , yet opportunities for prevention are often missed . METHOD We provided an automatic electronic record preventive care reminder system for  12 preventive care activities for one 10 doctor practice . All patients who attended were r and omised by the terminal digit of their record number . RESULTS The control uptake of opportunistic prevention was low ; ranging from 1.5 % ( tetanus immunisation ) to 27 % ( influenza immunisation ) . The reminders increased this by significant but small amounts for four out of 12 activities ( immunisation for tetanus and pneumococcus and recording of allergies and weight ) , insignificant increases for four ( mumps , measles and rubella immunisation , recording of smoking , and taking of cervical smears and of blood pressure ) , and insignificantly decreased influenza immunisation , and screening for diabetes and hyperlipidaemia . DISCUSSION Opportunistic electronic reminders have the potential to increase preventive care in general practice BACKGROUND The role of portable cholesterol analyzers in the identification and management of hypercholesterolemia is controversial . This study investigated the effect of free office cholesterol testing on screening behavior and on blood cholesterol reduction in a family practice center . METHODS After a baseline period of 5 months , an office cholesterol analyzer was made available for 1 year to two teams of patients and providers ( study group ) , but not to the other two teams ( control group ) . RESULTS The percentage of patients screened increased from 28 % to 52 % in the study group , and from 29 % to 42 % in the control group ( difference favoring study group , prevalence odds ratio = 1.47 , 95 % confidence interval [ CI ] = 1.33 to 1.62 ) . Compared with those whose cholesterol tests were sent to outside laboratories , patients screened with the office analyzer were younger ( mean age 36 years vs 42 years ) , and the barrier to those without insurance was reduced . There was no clinical ly or statistically significant effect on lowering cholesterol ( difference favoring study group = 0.01 mmol/L , 95 % CI = -0.15 to 0.17 ) . CONCLUSIONS The availability of free office cholesterol testing increased the prevalence of cholesterol testing , particularly for younger patients and those without insurance ; however , the testing had no discernible effect of motivating patients to lower their blood cholesterol levels In a r and omized , controlled trial ( N = 8,298 ) the authors compared three ways of encouraging patients in a large family practice to obtain a blood pressure check . Working from computerized medical records at a teaching familymedicine center , the study included patients aged 18 years and over who had not had a blood pressure measurement during the previous year . In a normalcare control group , 21.1 % of those due for a blood pressure reading obtained one during the trial year . In one intervention group , a computer-generated message reminded the doctor to check the  blood pressure of patients who happened to visit the center for an appointment ; 30.7 % of patients allocated to this group were checked . In the second group , the nurse contacted patients by telephone and encouraged 24.1 % of those who were due for a check to obtain one . Sending a letter to patients in the third group yielded a 35.7 % compliance rate . Considering the costs of the three interventions , the physician reminder was the most cost effective , followed by the letter reminder . Although statistically significant , the impact of the reminders was modest . A better approach might involve a combination of routine reminders to the physician , followed by letters to noncompliant patients
target: Conclusions Interventions using physician reminders , using dedicated personnel to deliver screening , and provision of financial incentives were found to be effective in increasing CVD risk factors screening uptake
background: Background Cardiovascular disease ( CVD ) is the leading cause of death globally .
  However , many individuals are unaware of their CVD risk factors .
  The objective of this systematic review is to determine the effectiveness of existing intervention strategies to increase uptake of  CVD risk factors screening .