Effect of a Patient Panel-Support Tool on Care Delivery

Objective: To evaluate the effect of a patient panel-support tool (PST) on care delivery for diabetes mellitus (DM) and cardiovascular disease (CVD).

Study Design:

Retrospective longitudinal cohort study among primary care providers (PCPs), with 2005 as the preintervention, 2006 as the implementation, and 2007 as the postintervention period.

Methods:

We estimated the intervention effect using electronic medical record data and hierarchical linear models. The intervention was a PST displaying "care gaps" and recommendations for glycosylated hemoglobin, low-density lipoprotein cholesterol, and blood pressure screening and control; retinopathy, nephropathy, and foot screening; aspirin, statin, and angiotensinconverting enzyme inhibitor or beta-blocker use; and influenza and pneumococcal vaccination. Participants were qualifying PCPs and health maintenance organizations; patients. Patients had DM or CVD and 12 months of membership (n =30,273 DM; 26,414 CVD). Main measures were mean percentages of care recommendations that were met by PCPs per patient per month (the care score).

Results:

From 2005 to 2007, the mean care score (95% confidence interval) increased for both DM and CVD, from 63.5 (62.7, 64.3) to 70.6 (69.8, 71.4) and from 67.9 (67.2, 68.7) to 72.6 (71.9, 73.3), respectively. After adjustments, DM and CVD patients had improvements in the care score of 7.6 and 5.1, respectively, in 2007 compared with 2005 (P <.001).

Conclusions:

Delivery of care recommendations for DM and CVD improved after implementation of a PST. More research is necessary to optimize results and determine whether patient outcomes improve.

(Am J Manag Care. 2010;16(10):e256-e266)

The effect of a patient panel-support tool (PST) on care delivery for diabetes mellitus (DM)and cardiovascular disease (CVD) was evaluated in a retrospective longitudinal cohort study among primary care providers.

• The mean percentage of care recommendations met (care score) increased for DM and CVD from 63.5 to 70.6 and from 67.9 to 72.6, respectively.

• Future work is needed to maximize the effects of PSTs on care recommendations met and to determine whether improvements in recommendations met translate to improved patient outcomes.

Electronic medical records (EMRs) hold great promise for changing care management by improving implementation of care guidelines and providing data and decision support to prompt and facilitate action.^1,2 Electronic tools utilizing EMR data such as alerts, reminders, and other embedded guideline advice have proved effective at improving clinical practice.³ However, many tools address a single group or a small group of care needs, and depend on a defined patient encounter (visit or phone call) to be useful to practitioners. The need for more global tools to harness data and technology stored in EMRs is particularly evident when treating patients with chronic diseases. For example, patients with diabetes mellitus (DM) or cardiovascular disease (CVD) receive only about 50% of the recommended care for which they are eligible.^4-7 These pervasive care gaps in screening, monitoring, preventive medication use, and risk factor control7-11 result in unnecessary morbidity, mortality, and healthcare costs.^5-7

We evaluated the effect of a new tool, the panel-support tool (PST),¹² on delivery of care recommendations by primary care providers (PCPs) for patients with DM and/or CVD. The PST uses EMR data to graphically display “care gaps” for each patient based on current evidence and provides summary information to assist practitioners and care teams in evaluating these gaps and ordering needed services. Such tools have been referred to as “quality dashboards”^13,14 and are becoming integrated into available EMRs¹⁵ or are in the development phase in homegrown systems.^14,16-21 However, despite their potential to be of enormous use to managed care organizations, published reports evaluating their success are limited.²¹

METHODS

Study Setting and Data Sources

The study was conducted in 2008 at Kaiser Permanente Northwest, a nonprofit group-model health maintenance organizations (HMO) operating in southern Washington and northern Oregon, with 15 medical clinics and 485,000 members. Members are demographically similar to the area population.²² Electronic databases provided information on clinician characteristics and patient membership, demographics, diagnoses, medications dispensed, over-the-counter medication documented by clinicians, blood pressure (BP), laboratory results, and procedures from inpatient and outpatient settings. These data captured more than 95% of all medical care and pharmacy services received by members.²² A fully electronic medical record (Kaiser Permanente HealthConnect, Epic System Inc) has been in place since 1996. A 99.5% specific diabetes registry²³ has existed since 1989; its sensitivity is estimated at 99% compared with expert chart review. A CVD registry derived from diagnoses and procedures (including acute myocardial infarction, ischemic heart disease or angina, coronary artery bypass procedures, percutaneous transcoronary angioplasty, stroke, and peripheral vascular disease) also has been in place since 1996. The study site’s clinical guidelines for diabetes and CVD care are consistent with the American Diabetes Association (ADA) guidelines⁸ and the American Heart Association’s guidelines.²⁴ During the study period (and as implemented in the PST), the ADA recommended aspirin for primary prevention in DM in those older than 40 years. More recent evidence led the ADA to recommend aspirin only in the presence of at least 1 additional CVD risk factor.²⁵ Other than the implementation of the PST, care processes for patients with DM and CVD remained stable during the study period.

The protocol for this study was approved by the institutional review board within the study HMO.

Panel Support Tool

Figure 1

The PST () has been previously described in detail.¹² It is Internet-based and graphically displays care gaps for each patient in a PCP’s panel, based on current evidenceof recommended care.^8,24,26 Sorting functions allow care teams to select patients for follow-up based on several parameters. The PST provides data on gaps in screening, medication use, monitoring, risk factor control, and immunizations, coded by color and number according to urgency. Gaps in medication use and risk factor control were emphasized through use of higher care gap scores for these services. For preventive needs, green indicates up-to-date; yellow, due soon; and red, overdue. For cardiovascular risk factor control, green indicates good control (eg BP, <135/85 mm Hg; glycosylated hemoglobin [A1C] <7%; low-density lipoprotein cholesterol [LDL-C] <100 mg/dL); yellow indicates modest elevations; and red indicates poor control. Prompts recommend conducting a screening test, considering starting/restarting a preventive medication, adjusting a medication dose, or adding an additional control medication, as indicated. Sets of clinical orders addressing each care gap facilitate clinician follow-up. Graphical displays allow care teams to compare their performance with that of other teams. Primary care providers and teams were trained in use of the PST and were expected by the HMO to use it to maximize team-member contributions during the implementation year (2006). Use of the PST by PCP teams gradually increased to 50% of teams by August 2006, 65% by December 2006, and more than 95% by February 2007. Although the PST was designed for use during clinic visits and for patient outreach, during the study period the training and implementation protocols were for use of the tool during visits.

Study Design and Population

This retrospective longitudinal cohort study evaluated the effectiveness of the PST at improving rates at which PCPs met care recommendations for patients with DM or CVD. Our unit of analysis was the PCP. Separate analyses were conducted for DM and CVD, and patients could be in one or both analyses.

We identified all 204 adult PCPs (in internal medicine or family practice) who had patient panels during all 3 time periods: the pre-PST period (2005), the PST implementation period (2006), and the post-PST period (2007). To achieve stability in our estimates, we limited analyses to the 167 PCPs who had at least 20 eligible DM patients during each study month and the 143 PCPs who had at least 20 eligible CVD patients each study month.

The study period was divided into 36 months, and data for each PCP were extracted monthly based on eligible patients in that month. Patients were eligible if they were at least 18 years of age and known to have DM or CVD. To enable the collection of covariates and outcomes, we also required that patients be continuous members during and 11 months before the study month.

Study Variables

Table 1

The primary outcome was the mean percentage of care recommendations met by PCPs per member per month (for their eligible patient panel each month) or the “care score,” with all included care recommendations weighted equally. Therefore, the care score could range from 0 (none of patients’ care recommendations were met) to 100 (100% of patients’ care recommendations were met). The composite outcome mirrored that used by others in DM and CVD^21,27 and in analyses that aimed to measure improvement over multiple clinical care needs.^28,29 The care recommendations were consistent with national guidelines^8,24 and mirrored the decision support constructed in the PST (). We included all screening, preventive medication use, risk factor control, and immunizations relevant to the care of DM or CVD that also were addressed by the PST: A1C, LDLC, BP screening and control (unmeasured values were considered to be uncontrolled); retinopathy, nephropathy, and foot screening; aspirin, HMG-CoA reductase inhibitor (statin), angiotensin-converting enzyme (ACE) inhibitor or beta-adrenergic blocker (betablocker) use; and influenza and pneumococcal vaccinations. Care recommendations not specific to this population were excluded (eg, cancer screening). To aggregate data at the PCP level for the care score, we determined the total number of services for which each member of the PCP’s patient panel was eligible and the percentage of those services that were met each month. Results for each individual care score component (percent met) also were calculated.

We included a series of covariates (PCP and patient panel characteristics and measures of healthcare utilization) that could impact the likelihood of care quality improvement resulting from the PST. The PCP demographics were determined at baseline (January 2005) and included age, sex, number of years employed at study site, adult primary care specialty (family practice or internal medicine), and degree type (medical doctor or allied health [nurse practitioner, physician’s assistant]). At the beginning of each study year, we determined the following PCP patient panel characteristics: size, percentage of patients with condition of interest (DM or CVD), mean age of patients, percentage of patients who were nonwhite, and percentage of the DM or CVD population with an annual family income of less than $40,000. Individual racial categories were obtained from electronic databases for 23,453 (78.1%) patients with DM and 23,647 (90.1%) patients with CVD, and missing race information was geocoded (estimated from neighborhood measures of socioeconomic status using the census-tract block corresponding to each subject’s mailing address).³⁰ Family income also was obtained from geocoded data.

For healthcare utilization variables that were likely to change more frequently and that provided opportunities for meeting care needs, at the beginning of each study month we determined the following additional PCP patient panel characteristics, using eligible patients of interest (DM or CVD) that month: mean number of outpatient specialty visits likely to impact outcomes (ie, endocrinology, cardiology, pulmonology, nephrology), mean number of outpatient visits to the assigned PCP, mean number of outpatient visits to other PCPs (per patient), percentage of patients with a hospitalization, and percentage of patients in selected care management programs likely to impact outcomes (specifically, nurse and pharmacist cardiovascular risk factor management, lipid or diabetes programs) in the prior year.

Statistical Methods

We used 3-level hierarchical linear models to determine whether the care score changed across the 3 time periods (pre-PST year, PST implementation year, and post-PST year), controlling for relevant factors. The first level of the model inn cluded variables that were measured monthly (time in months and utilization measures); the second level included variables that were measured annually (panel size and patient demographics and indicators of the PST period [PST implementation vs pre-PST, post-PST vs pre-PST]); and the third level included characteristics of the PCP. To achieve parsimonious models, we used a stepped process to determine variables to retain in the models. First, we developed the list of variables that could impact the likelihood of care quality improvement resulting from the PST, as noted above. Then at the first level of the model, we retained those utilization covariates (mean number of PCP visits, mean number of specialty care visits, and percentage of patients in selected care management programs) that made a significant contribution to the care score. Next, we added the second level to the model, retaining the panel characteristics (for DM/CVD patients: number per PCP, the proportion of DM/CVD patients in the PCP’s panel, mean age, and the proportion that were nonwhite) that were significant. In the last step, the third level of the model was added, retaining characteristics of the PCP (PCP age, years in the plan, and degree) that were significant. This stepped approach allowed us to include theoretically relevant variables as covariates without overfitting the model. Time (in months) by period (pre-PST, PST implementation, post-PST) interaction was included in the model to test whether the rate of change from January to December of each year differed for the 3 periods. We expected to see very little monthly change in the pre-PST period; a greater monthly change during the PST implementation period, as more clinics and PCPs were trained; and a leveling off of the monthly change in the post-PST period. A base rate for time by period interaction was included to test whether differences between periods (in monthly rates of change) varied according to PCPs’ starting care score. All analyses were conducted with SAS 9.1 (SAS Institute Inc., Cary, NC) or HLM 6.0 for Windows (Raudenbush SW, Bryk AS, Congdon R. Lincolnwood, IL: Scientific Software International, Inc; 2004).

RESULTS

A mean of 20,705 (SD = 1369; range 18,401-22,574) patients per month across all PCPs and a mean of 123 (SD = 55; range 22-285) patients per month per PCP qualified for the DM analysis. A mean of 17,840 (SD = 1464; range = 15,168-19,473) patients per month across all PCPs and a mean of 124 (SD = 61; range 23-302) patients per month per PCP qualified for the CVD analysis. A total of 30,473 unique DM patients and 26,414 CVD patients were included in the monthly calculations across the 3 time periods (8543 were in both analyses).

Table 2

Characteristics of PCPs and their patients are presented in . The PCPs in both analyses were similar in mean age and years of practice. The PCP panel patient characteristics were fairly stable through the study period. The mean total panel size was approximately 1500 patients, and PCP panels were about 9% DM and 8.7% CVD patients. The mean age of DM patients was 61 years, compared with 70 years for CVD patients. The DM patients were about 12% nonwhite, compared with 7% of CVD patients, and nearly 20% of patients in both analyses had a family income of less than $40,000 per year. Both patient groups had similar numbers of mean annual visits to primary care clinics, but CVD patients used selected specialty visits, hospitalizations, and care management programs at nearly double the rate of DM patients.

Table 3

shows the unadjusted care scores and the individual components of the composite measure across the study years. The care scores across the study years (95% confidence interval) increased for DM patients from 63.5 (62.7, 64.3) pre-PST in 2005 to 65.7 (64.9, 66.6) during implementation in 2006, and to 70.6 (69.8, 71.4) post-PST in 2007. Corresponding increases for CVD patients were from 67.9 (67.2, 68.7) to 69.4 (68.7, 70.2), and then to 72.6 (71.9, 73.3). Improvements in the composite measure were largely due to improvements in rates of nephropathy screening, foot exams, all medications used for secondary prevention (clinician documentation of aspirin use and dispensing of statins, beta-blockers, and ACE inhibitors/angiotensin II receptor blockers), LDL-C control, and pneumococcal vaccine use. Blood pressure control showed small improvements, and A1C control declined.

Table 4

Figure 2

presents modeling results of the care score (in DM and CVD analyses), adjusting for selected healthcare utilization, PCP, and patient panel size and demographic characteristics. For DM, the main effect for time period shows that, controlling for other variables, during the PST implementation year PCPs had an absolute improvement in the mean care score of 2.3 (coefficient or b = 2.3, P <.001) compared with the pre-PST year; in the post-PST year, the care score improved 7.6 (b = 7.6, P <.001). For CVD, PCPs had an absolute improvement in the care score of 1.6 during the implementation year (b = 1.6, P <.001) and of 5.1 in the post-PST year (b = 5.1, P <.001). The difference in care score predicted by the models with covariates is very similar to the observed unadjusted difference in care scores between the periods (DM: improvement of 2.2 in the implementation year and 7.1 post-PST; CVD: improvement of 1.5 in the implementation year and 3.2 post-PST). Time-by-period interactions reveal that during PST implementation, the care score began to increase at a rate of 1.7 per month in DM (b = 1.7, P <.001) and 2.1 per month in CVD (b = 2.1, P <.001) compared with the prior year. These findings, which are graphically displayed in , confirm our hypothesized pattern of results: flat performance pre-PST, rapid improvement during implementation, and a slower continued rate of improvement postimplementation. A separate sensitivity analysis including only CVD patients without DM found the same pattern (data not shown).

Several other factors significantly affected the care scores for both patient cohorts. The PCPs with higher baseline care scores had smaller increases in the rate of monthly change from pre-PST to PST implementation period for both DM (b = -0.02, P <.001) and CVD (b = -0.03, P <.001). Having more selected DM- and CVD-relevant outpatient specialty visits, being enrolled in care management programs, and higher number of visits to the patient’s assigned PCP per month were associated with higher care scores (P <.001 for all). The care score was 1.9 higher in DM patients and 1.4 higher in CVD patients for each additional specialty visit, and it was 2.6 higher in DM patients and 1.5 higher in CVD patients for each additional visit to the patient’s PCP. Also, for each additional year of mean panel age, the care score was 0.41 higher in DM (P <.001) and 0.39 higher in CVD (P <.001).

DISCUSSION

We found that implementation of a PST increased the percentage of care recommendations that were met (the care score) for patients with DM and/or CVD. In the post-PST year (2007), patients with DM had an absolute improvement in the mean care score of 7.6, and CVD patients had an absolute improvement in the mean care score of 5.1. The absolute improvements observed from the start of implementation to the end of the study period represent a relative improvement in the care score of about 14.3% in DM and 10.6% in CVD. The fact that the CVD group started with a higher baseline care score suggests a possible ceiling effect, rather than that the PST is less effective at increasing the number of recommendations met in this group. This implies that managed care organizations with lower baseline performance may achieve greater benefit for their CVD patients by using a PST.

Findings from clinical trials support the significance of improvements in preventive medication use and control of 3 of the 4 CVD risk factors that we observed. Aspirin,^31,32 statins,^33-36 and ACE inhibitors^37,38 reduce morbidity and mortality in high-risk patients; beta-blockers improve outcomes in patients with heart failure²⁴; and control of BP and LDL-C are proven strategies for delaying the progression of microvascular disease in patients with diabetes and macrovascular complications in both groups.⁸

In this study, A1C did not improve and in fact worsened post-PST. We are unable to say definitively why this occurred, but the study site guidelines and protocols may have more strongly emphasized the use of aspirin, ACE inhibitors, and statins as opposed to improving glycemic control alone. Alternatively, given limited resources, some care improvements occur at the expense of others. However, in light of recent studies showing no cardiovascular benefit from intensive glycemic control, focusing attention on proven therapies may be the better choice.^39-41

Improvements in screening and immunizations were less consistent relative to improvements in medication use and risk factor control. These areas are somewhat de-emphasized by the PST, which may explain the outcome. Frequency of foot exams in DM patients improved, as did nephropathy screenings and receipt of pneumococcal vaccines.^26,42,43

This study builds on the findings of prior studies evaluating effects of alerts, reminders, and other decision-support tools on treatment of patients with DM or CVD.^44-47 Our process-measure results are similar to those recorded in systematic reviews of trials of computer-assisted management systems in these populations. One review noted that 5 of 7 trials (71%) in DM and 5 of 13 (38%) in CVD showed improvements in care44; process improvements were generally in the 12% to 20% range.⁴⁶ However, evaluations of the effects of such systems have generally been limited to assessing either DM or CVD, and have included only small groups of physicians and patients.⁴⁴ One study included nearly 7000 patients and addressed both DM and CVD,²⁷ and a Veterans Administration reminder study included nearly 13,000 patients and several care prompts relevant to both groups.²⁹ A recent observational study of a quality-dashboard tool and planned chronic illness visits improved diabetes quality measures, but the separate effect of the dashboard could not be assessed.²¹

One notable difference between other studies and ours is that patient outcomes in other studies, when reported, largely did not improve, with the exception of improvement in diastolic BP in 1 study⁴⁸ and lipid levels in another.⁴⁹ Although we cannot say absolutely why BP and LDL-C control improved in our study, an advantage of the PST is that staff assisting clinicians can access care gaps and act on them at office visits, by telephone, and by electronic mail.

This study has limitations. Although the study has a strong quasi-experimental design accounting for preintervention trends, it is not a randomized controlled trial. As a result, we cannot completely eliminate the possibility that concomitant unmeasured factors may explain some of the observed changes. However, other processes and services for patients with DM and CVD remained stable during the study. Also, our study was conducted at a single integrated HMO, and our findings may not be generalizable to other settings. However, a PST can certainly be implemented in most managed care settings with an EMR.

In summary, to our knowledge this is the first study to find that a PST can be successfully integrated into care of large populations of patients with DM and CVD to improve delivery of patient care recommendations. Given that gaps in delivery of care for these complex patients are common, the implementation of a PST could have a substantial impact on patient care. Future studies should evaluate optimization of the tool, its effect on other clinical domains, and which practices may further improve results.

Acknowledgments

We would like to thank the many individuals who assisted with the design and conduct of the study and who were critical organizational advocates for the patient support tool: Maureen Wright, MD; Homer Chin, MD; Thomas Hickey, MD; Wiley Chan, MD; Michael Krall, MD; and Trung Vu, BA. Adrianne Feldstein, MD, MS, principal investigator, had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

We also thank Mary Rix, RN, and Gail Morgan for project management; Weiming Hu, MS, for research analyst support; Leslie Bienen, DVM, for editorial assistance; and Dixie Sweo for administrative support.

Author Affiliations: From Kaiser Permanente Northwest (ACF, NAP, RU, AGR, GAN, DHS, JS, CMD, NLL), Portland, OR; and Kaiser Permanente Program Office (YYZ), Oakland, CA.

Funding Source: This project was supported by a Kaiser Permanente electronic medical record research initiative. The funding organization was not involved in the design or conduct of the research; the collection, management, analysis, or interpretation of the data; or the preparation or approval of this manuscript.

Author Disclosures: Dr Nichols reports having pending and received grants from GlaxoSmithKline, Merck & Co, Takeda, and Tethys Bioscience. The authors (ACF, NAP, RU, AGR, DHS, JS, CMD, YYZ, NLL) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (ACF, NAP, RU, GAN, DHS, JS, YYZ, NLL); acquisition of data (ACF, AGR, JS); analysis and interpretation of data (ACF, NAP, RU, GAN, DHS, JS, CMD, NLL); drafting of the manuscript (ACF, NAP, DHS, JS, CMD); critical revision of the manuscript for important intellectual content (ACF, NAP, RU, GAN, DHS, JS, YYZ); statistical analysis (NAP, AGR); provision of study materials or patients (ACF); obtaining funding (ACF, NAP); administrative, technical, or logistic support (ACF, RU, CMD, NLL); and supervision (ACF).

Address correspondence to: Adrianne C. Feldstein, MD, MS, Center for Health Research, Kaiser Permanente, 3800 N Interstate Ave, Portland, OR 97227. E-mail: adrianne.c.feldstein@kpchr.org.

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