The article by Peikes et al in this issue of 

 (“Early Evaluations of the Medical Home: Building on a Promising Start”)

 critiques the findings of several recent studies of different models of “the medical home.” As lead investigators of one of these studies—the matched-pair, cluster-randomized evaluation of Guided Care&mdash;we were drawn to their discussion about the potential analytic effects of “clustering” (ie, the tendency for the outcomes of patients treated by the same physician or practice to be more similar than the outcomes of patients treated by different physicians or practices). For cluster-randomized studies, Peikes et al classify analyses that did not adjust for clustering as being “inconclusive” or of “uncertain statistical signifi cance” when the reported results were statistically significant (ie, 

Their categorization of our findings is understandable. However, given that our evaluation enrolled only 7 matched pairs, it begs the question of how our analysis should account for clustering. More importantly, how should small, matched-pair, cluster-randomized studies of the future account for clustering, so that they can produce useful information about the effects of medical homes and other provider-based interventions?

Statisticians usually consider 2 strategies for analyzing matched-pair, cluster-randomized data: (1) site-level adjustment and (2) patient-level adjustment.

 For studies with small numbers of sites (in our case, matched pairs), the former approach is recommended. In this approach, the intervention-specific outcomes (or residuals, when covariate adjustment is required) within each matched pair are summarized into a scalar quantity (eg, mean). Statistical inference is then based solely on these pairs of numbers.

 argues that this approach can yield confi dence intervals and 

 values that are too conservative. In order to maximize the information content from small, matched-pair, cluster-randomized studies, it is essential that the inferential procedure be as precise as possible.

We intend to revisit our original analyses as well as the analytic strategy for the final results of the Guided Care trial that are to be reported later this year. We continue to

believe that small, matched-pair, cluster-randomized studies, which are more feasible within the context of our resource-constrained environment, can provide useful evidence to guide learning on patient-centered medical homes.

The authors would like to thank Constantine Frangakis, Thomas Louis, PhD, and Brian Caffo, PhD, from the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and Kosuke Imai, PhD, from the Department of Politics, Princeton University, for their assistance in the preparation of this editorial.

Johns Hopkins Bloomberg School of Public Health

Division of Geriatric Medicine and Gerontology

Johns Hopkins University School of Medicine

Department of Health Policy and Management

The authors (DOS, CMB, JLW, CB) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Daniel O. Scharfstein, ScD, Professor, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Rm E3547, Baltimore, MD 21205. E-mail: dscharf@jhsph.edu.1. Peikes D, Zutshi A, Genevro JL, Parchman ML, Meyers DS. Early evaluations of the medical home: building on a promising start. Am J Manag Care. 2012;18(2):105-116.

2. Hayes RM, Moulton LH. Cluster-Randomised Trials. Boca Raton, FL: Chapman & Hall/CRC Press; 2009:233-254.

3. Imai K, King G, Nall C. The essential role of pair matching in clusterrandomized experiments, with application to the Mexican Universal Health Insurance Evaluation. Stat Sci. 2009;24(1):29-53. 

Articles

LETTERS TO THE EDITORS

TO THE EDITORS:

To assess the feasibility of engaging adults with diabetes in self management behaviors between clinic visits by using cell phone text messaging to provide blood sugar measurement prompts and appointment reminders.

 Quasi-experimental pilot among adult diabetic patients with cell phones who receive regular care at a federally qualified community health center in Denver, Colorado, which serves a population that is predominantly either uninsured (41%) or on Medicaid or Medicare (56%).

Patients (N = 47) received text message prompts over a 3-month period. Blood sugar readings were requested 3 times per week (Monday, Wednesday, and Friday). Reminders were sent 7, 3, and 1 day(s) before each scheduled appointment. Acknowledgments were returned for all patient-sent messages. Focus groups were conducted in English and Spanish with selected patients (n = 8).

 Patients of all ages were active participants. Correctly formatted responses were received for 67.3% of 1585 prompts. More than three-fourths (79%) of the cohort responded to more than 50% of their prompts. The appointment analysis was underpowered to detect significant changes in attendance. Participants reported increased social support, feelings that the program “made them accountable,” and increased awareness of health information. Two-thirds (66%) of patients provided glucose readings when prompted during the study, compared with 12% at 2 preceding clinic visits.

 For certain patients, cell phone—based text messaging may enhance chronic disease management support and patient-provider communications beyond the clinic setting.

Healthcare systems can better support chronic disease management by offering patients customized communication that identifies and supports patients outside the clinic visit setting.

Health information technology (HIT) can be used to provide needed support to patients for chronic disease management.

In this study, blood sugar measurement prompts and appointment reminders were provided to adult patients with diabetes through cell phone text messaging to promote engagement in self-care behaviors.

Low-cost, mobile HIT such as that used in this study may feasibly enhance regular patient-provider communications and engagement with the healthcare system for some patients.

The impact of chronic disease is substantial. An estimated 60% of deaths worldwide are thought to be caused by chronic disease,

 with 4 chronic diseases—cardiovascular disease, cancer, chronic respiratory disease, and diabetes&mdash;responsible for 29 million deaths in 2002 alone.

 The situation is no better in the United States, where 70% of deaths annually are attributable to chronic disease

 and almost 50% of the population has at least 1 chronic illness.

Patients need to manage chronic diseases on a daily basis, yet US healthcare systems are not well structured to support self management across large patient populations.

 Both patients and providers express frustration with the standard approach to managing chronic disease through the 20-minute provider-driven clinic visit.

 In addition, traditionally vulnerable and medically underserved groups, which are more likely to have poorly controlled chronic illness,

 are also more likely to experience barriers in accessing healthcare, which means that chronic disease management strategies focused on only during the clinic visit present further challenges for these patients.

Health information technology (HIT) has been shown to be useful in helping patients to manage chronic conditions outside the clinic setting.

 The combination of Web-based health informatics with case management strategies has been reported to improve blood pressure control among patients with hypertension

 and glycemic control among patients with diabetes.

 Cell phone—based text messaging has also been associated with improved glycemic control when used to assist with diabetes case management.

 The latter is of particular interest given that high rates of cell phone access are reported among groups in the United States that exhibit low rates of computer and Internet use,

 including 71% of blacks and 59% of all Latinos, both English-dominant and Spanish-dominant.

 Cell phone access has also been associated with knowledge of health information.

This article describes the results of a pilot study conducted to evaluate the feasibility of utilizing low-cost, mobile HIT to support chronic disease self management between clinic visits among patients in an urban safety net setting. Cell phone text messaging was used to provide blood sugar measurement prompts and appointment reminders to adult patients with diabetes in order to promote engagement in self-care behaviors.

The quasi-experimental pilot study was conducted among English-speaking and Spanish-speaking adult patients with diabetes who have access to cell phones and who regularly receive primary healthcare at Sam Sandos Westside Family Health Center (Westside), 1 of 8 federally qualified community health centers in the Denver Health integrated healthcare system in Denver, Colorado. Westside serves a predominantly Latino population (81%) that is largely either uninsured (41%) or on Medicaid or Medicare (56%).

The Colorado Multiple Institutional Review Board approved this study prior to enrollment and implementation activities. All English-dominant and Spanish-dominant adult patients included in the Denver Health diabetes registry were identified as potential study participants. Westside providers and clinic staff confirmed potential participants’ access to cell phones at clinic visits and referred patients to the study nurse for recruitment. All patients signed a written consent prior to participation.

A software platform, the Patient Relationship Manager (PRM), was created in partnership between Denver Health, EMC Consulting, and Microsoft Corporation to handle text messaging activities. The PRM system was designed to automatically send text messages to patients according to an established schedule, and to process text message responses received from patients for appropriate action based on established threshold values.

Participants (N = 47) received text message prompts in their choice of English or Spanish over a 3-month period. Messages requesting fasting blood sugar readings were sent to each patient at 7:15 am on Monday, Wednesday, and Friday of each week during the study period, and appointment reminder messages were sent to each patient 3 times in advance of each scheduled appointment that patient had during the study period, at 7 days, 3 days, and 1 day prior to the appointment date. Blood sugar requests included the day of the week and calendar date that the message was sent and a request for the patient to respond with his or her fasting blood sugar that day in numeric values. Appointment reminders included the time, date, and location of the upcoming appointment along with a request for patients to respond with “Y” or “N” to indicate whether or not they planned to attend the appointment. The system automatically returned an acknowledgment of receipt in response to all patient-sent messages. Examples of all message types are shown in 

Misformatted responses and fasting blood sugar values reported by patients as either above or below an established clinical range of 70 to 400 mg/dL were automatically flagged by PRM and routed to a work queue for further action. A registered nurse dedicated to the study at 0.2 full-time equivalents reviewed all flagged messages and contacted patients by telephone for follow-up assessment. The study nurse problem-solved message formatting issues with patients and presented out-of-range values to a physician champion, who helped formulate a targeted response plan to improve the patient’s glucose control (eg, by changing eating patterns or adjusting medications). The nurse also ensured that both telephone encounters and patient-reported blood sugar measurements were appropriately documented in the medical record.

Outcome measures included appointment attendance rates and patient response rates to text message prompts. A power analysis was performed using Power Analysis and Sample Size (PASS 2008) software (NCSS, Kaysville, Utah), with the significance level (alpha) targeted at .05 with greater than 90% certainty to identify the number of appointments needed to detect a significant absolute difference of 10% in preintervention and postintervention attendance rates. Clinical chart review for all study patients was undertaken for the last 2 clinic visits prior to study enrollment to assess how often patients discussed home glucose readings with their providers.

Focus groups were conducted with patients in order to assess satisfaction with the HIT intervention program by soliciting feedback on the experience and recommendations for future improvement. Two focus groups were held, 1 conducted in English and 1 in Spanish with the aid of an experienced medical interpreter. Both group sessions were audio recorded. Recordings were supplemented with written notes taken by the group facilitator. Focus group data were obtained through inductive analysis of the audio recordings and written notes. Emergent themes and patterns among patients’ remarks were identified and synthesized into a summary report.

Demographics of both study participants and the overall population of Westside patients with diabetes are presented in 

. Patients of all ages were active participants in the program,with the majority of participants aged 40 to 49 years (n = 14), 50 to 59 years (n = 17), and 60 to 69 years (n = 9). The intervention group was significantly younger than the clinic population, but well matched for sex and racial/ethnic background.

A total of 1585 text messages requesting a response were automatically sent to patients by the PRM system. Patients responded to prompts with a total of 1080 text messages (68.14%), of which almost all were correctly formatted (1066 messages; 98.7%). More than three-fourths (79%) of the cohort responded regularly to more than 50% of their message prompts. Home glucometer result availability was significantly improved among study participants. Two-thirds (66.4%) of study patients provided correctly formatted blood sugar values in response to medical measurement text message requests received during the study period. By contrast, chart review data for each study participant indicated that providers were able to review home glucometer readings with patients only 12% of the time during the last 2 clinic visits prior to the intervention.

Despite reminders for 69 appointments during the intervention, cancellation rates and no-show rates were 21% and 14%, respectively, and did not differ from preintervention rates. However, the power analysis indicated that a sample size of 349 appointments was needed to achieve adequate power to detect a significant difference in appointment attendance rates.

A total of 8 patients (6 female and 2 male) participated in 2 focus groups. Among these participants, 2 were aged 30 to 39 years, 2 were aged 50 to 59 years, and 4 were aged 60 to 69 years. Two participants were Spanish dominant and the remaining 6 participants were English dominant. Half of the focus group participants were white, and 37.5% were Latino. Participants’ comfort level with program technology, perception of and satisfaction with the intervention program (including message type and frequency), and the perceived impact of the program on diabetes self management were explored as major domains in group discussions.

All 8 participants reported comfort with the process of text messaging, both in sending and receiving messages. Three participants identified text messaging as a means they already used for communicating with their children. One participant with vision impairment involved other family members to assist with reading and replying to messages. Three participants expressed a preference for text messaging over other computer-based communications such as e-mail.

Participants’ Satisfaction With the Program

All 8 participants viewed the text message program favorably, expressing both a liking for it and a desire for it to continue, along with a willingness to recommend it to others. The program was perceived as having expanded participants’ communication and engagement with the healthcare system; half of the participants remarked on feeling that their providers were “more connected” or otherwise aware of what was going on with them. Financial costs associated with text messaging were perceived by participants to be negligible; all participants reported having unlimited text messaging plans. Five participants indicated that they would be willing to pay for program messages even if they did not have unlimited plans.

All participants found the frequency of text messaging during the study period to be acceptable. Half of the participants expressed interest in receiving daily messages, while the other half were content with receiving messages according to the current schedule. No one wanted to receive messages less often. Four participants expressed specific appreciation for being able to respond to messages throughout the day on which they were requested, according to the patient’s own time frame and personal schedule. Six participants noted that they also liked the acknowledgment from the system that their responses had been received. Although all participants received self-management support messages, not all received appointment reminders. Two who did expressed appreciation for them, and the others indicated interest in receiving them.

Program Impact on Diabetes Self Management

In addition to a general overall perception of usefulness, the program was specifically considered to be helpful in establishing regular morning routines (4 participants) and in improving recollection of and adherence to self-management activities (4 participants). Participants reported increased awareness of their health information (2 participants) and feelings of being made accountable for their choices (4 participants), along with better perceived social support either from family (2 participants) or from the system itself (3 participants) in the absence of other established social groups. Three participants observed that they would like additional, personalized feedback about their performance on self-management activities.

In this quasi-experimental pilot study, we assessed the feasibility of engaging both English-speaking and Spanishspeaking adults with diabetes in self-management behaviors between clinic visits by using cell phone text messaging to provide blood sugar measurement prompts and appointment reminders. Over the course of the study, there was a sustained high response rate of 68% to all text message prompts received, with 66% of patients providing home glucose data upon request and 79% of patients responding to more than half of their glucose prompts. Focus group participants categorically embraced the text message—based outreach.

This pilot intervention differed from usual care by engaging patients between clinic visits. Like many safety net health centers, Denver Health does not have the capability to support downloading home glucose reading data into the clinical system during clinic visits. Depending solely on clinic visits to support discussion of self-monitoring data is challenging as well: chart review of 2 clinic visits prior to the intervention for each study participant indicated that providers reviewed home glucometer readings only 12% of the time. Although in its infancy, text messaging has been used to improve the management of asthma, hypertension, and diabetes,

 but we are not aware of studies conducted in an indigent, predominantly Latino population.

The results of this pilot study demonstrate that there is potential for using cell phone text messaging to support chronic disease management and engagement in an underserved population. Additionally, the sustained response rate and consistent response patterns demonstrated among study participants enhances the ability of the patient and provider to engage in shared decision making at clinic visits by improving access to self-monitoring glucose data collected in sufficient volume to guide well-informed clinical decisions.

WEB EXCLUSIVE

Cell phoneâ€œbased text messaging may be used to feasibly support chronic disease management and engagement in diabetes self-care behaviors for some patients.

Care by Cell Phone: Text Messaging for Chronic Disease Management

There is a need for randomized, prospective trials of case management interventions with resource utilization analyses.

To determine whether algorithm-driven telephone care by nurses improves lipid control in patients with diabetes.

 Prospective, randomized, controlled trial.

Adults with diabetes at a federally funded community health center were randomly assigned to intervention (n = 381) or usual-care (n = 381) groups.

Nurses independently initiated and titrated lipid therapy and promoted behavioral change through motivational interviewing and self-management techniques. Other parameters of diabetes care were addressed based on time constraints.

The primary outcome was the proportion of patients with a low-density lipoprotein (LDL) less than 100 mg/dL. Secondary outcomes included the number of hospital admissions, total hospital charges per patient, and the proportion of patients meeting other lipid, glycemic, and blood pressure guidelines.

The percent of patients with an LDL <100 mg/dL increased from 52.0% to 58.5% in the intervention group and decreased from 55.6% to 46.7% in the control group (

 <.01). Average cost per patient to the healthcare system was less for the intervention group ($6600 vs $9033,

= .03). Intervention patients trended toward fewer hospital admissions (

= .06). The intervention did not affect glycemic and blood pressure outcomes.

Nurses can improve lipid control in patients with diabetes in a primarily indigent population through telephone care using moderately complex algorithms, but a more targeted approach is warranted. Telephone-based outreach may decrease resource utilization, but more study is needed.

The percent of patients with a low-density lipoprotein <100 mg/dL increased from 52.0% to 58.5% in the intervention group as compared with decreasing from 55.6% to 46.7% in the control group.

 Average cost per patient to the healthcare system was less for the intervention group ($6600) than for the control group ($9033).

Intervention patients trended toward fewer hospital admissions.

Diabetes is a disease growing in epidemic portions worldwide with associated costs that signifi cantly impact healthcare systems. In the United States more than 20 million persons have been diagnosed with diabetes, a number which at the current rate will increase to more than 30 million by the year 2030.

 Estimates of diabetes costs rose from $130 billion in 2002 to $174 billion in 2007, and are projected to reach $192 billion by 2020.

In our safety-net institution, Denver Health Medical Center (DHMC), the majority of patients with diabetes are of Latino ethnicity (59%) with a substantial minority being of African American race (21%). Most of the patients enrolled in our system have either no or inadequate insurance coverage. Consistent with national trends, the majority of our patients with diabetes do not meet the targets of care recommended by the American Diabetes Association (ADA) and National Cholesterol Education Program (NCEP) guidelines.

 These guidelines are based on numerous prospective interventional studies which demonstrated the delay or prevention of the microvascular and macrovascular complications associated with this disease.

The reasons for suboptimal performance on diabetes outcomes are complex and involve the patient, the provider, the systems of healthcare delivery, the ability to track and assist patient populations, and societallevel factors.

Potentially modifi able patient-level factors include lack of diabetes education, resources, and motivation. Competing demands in the 20-minute visit limit the provider’s ability to identify barriers and educate and motivate patients.

Nurse-run case management programs may overcome some of these chronic disease management barriers through frequent patient contact, tracking of patients, and the use of motivational interviewing and selfmanagement techniques. Recent reviews of diabetes disease-management programs point to mounting evidence for the benefi ts of nurse-run case management and targeted interventions in the clinic.

 There is a growing but insuffi cient study of telephone-based case management in diabetes. Telephone-based diabetes interventions have often focused on glycemic control, targeting the veteran population as well as elderly, ethnically diverse patients,

 and often focus on glycemic control. To date, however, we are not aware of any study of a nurse-run, telephone-based intervention to improve lipid control in a population with diabetes composed mainly of minorities with no or inadequate insurance coverage. Also, there is a call for large, prospective diabetes case-management interventions with resource utilization analyses.

Design, Setting and Participants, and Randomization

The design, setting and participants, and randomization flow are described in a previous publication.16 Briefl y, this randomized, controlled trial was conducted at Denver Health’s Westside Family Health Center (Westside Clinic), a federally funded community health center which serves a primarily indigent, Latino population. We included only adult patients (aged >17 years) in our diabetes registry who were actively utilizing Westside Clinic for their primary care (at least 2 visits in the past year) and who spoke either English or Spanish. We sought to maximize the generalizability of the study and therefore had only minimal exclusion criteria: pregnant or lactating women, patients with end-stage renal disease (creatinine >3.0 mg/dL), and/or a comorbid illness with life expectancy less than 12 months (eg, terminal cancer or Child’s-Pugh Class C hepatic cirrhosis).

The details of this intervention have been previously published.

Briefl y, this telephone outreach program lasted 20 months, ending in May 2007, and was considered an adjunct to usual care. At the time of this intervention, usual care consisted of largely primary care provider—driven diabetes care with most providers recommending clinic visits every 1 to 3 months for patients not at diabetes goal, and every 6 months for those at goal. The study nurses focused on lipid management, using algorithms based on published guidelines from the most current NCEP and ADA recommendations.

 The nurses independently checked labs and initiated and titrated lipid-lowering medications over the telephone with a 2-week follow-up call to assess for medication side effects and a 6-week followup call to recheck lipids after medication changes.

 The nurses were also trained in motivational interviewing techniques and facilitation of patient self-management. Additionally, the nurses used algorithms addressing other aspects of diabetes care which included glycemic and blood pressure control, update on vaccinations, and facilitation of primary care provider (PCP) and subspecialty appointments based on time constraints. The nurses used pre-printed prescriptions signed by a physician champion who offered educational and management support.

Relevant demographic and laboratory data were extracted from an integrated electronic health record for all patients. Comorbidities were determined based on all historical diagnosis codes that existed for the patients in our integrated system. Medication use was determined based on outpatient pharmacy records for prescriptions that were filled within our system in the 3 months leading up to study initiation.

The primary outcome is the proportion of patients (both with and without cardiovascular disease [CVD]) with a lowdensity lipoprotein (LDL) less than 100 mg/dL, secondary outcomes include: 1) average hospital charges per patient including inpatient, outpatient, emergency department, and intervention costs; 2) total number of inpatient admissions; 3) proportion of patients with CVD with an LDL less than 70 mg/dL per NCEP guidelines

; 4) percentage of patients with last blood pressure less than 130/80 mm Hg as recommended by The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure19 percentage of patients with glycated hemoglobin (A1C) less than 7 mg/dL per ADA guidelines.

We performed a conservative sample size estimate by assuming that 50% of control patients at study end would achieve the target LDL of less than 100 mg/dL (no improvement) as compared with 70% in the intervention group. Utilizing an alpha = 0.05, power of 90%, we estimated that we needed 129 patients in each randomization group. However, we expanded the sample size to 381 patients in each group since previous chronic disease management at DHMC suggested nurses could handle a panel of this size and allowed for cost analyses.

Differences in lipid, glycemic, and blood pressure outcomes were compared pre- and post-intervention for both the control and treatment groups using an intention-to-treat analysis. Patients without a lab value during the designated time period were counted as not being at goal for that time period. Multivariate logistic regression controlled for baseline lipid performance, age, gender, insurance, and ethnicity for the primary lipid outcomes. Statistical analyses were performed using SAS (version 9.1, SAS Institute Inc, Cary, North Carolina) software.

Measuring Healthcare Utilization and Costs

Nine separate multiple linear regression models were used to compare treatment and control groups over time according to the following utilization and cost measures: number of inpatient admissions; number of outpatient visits; number of emergency visits; total inpatient costs; total outpatient costs; total emergency costs; sum of all costs; and total cost per patient enrolled.

The linear models were adjusted to account for differences in age, race/ethnicity, gender, baseline levels for each outcome variable, and degree of illness, and included generalized estimated equations (GEEs) to account for the within-subject correlation of repeated measures by individual patients. Patients lost to follow-up, defi ned as no primary care visit in the year preceding the end of the study, were not assessed when comparing baseline with the intervention period, for consistency of results.

Costs were evaluated from the perspective of the healthcare system, which included case-management programmatic costs and the incremental costs associated with the change in the utilization of healthcare services. Cost-to-charge ratios were used to convert charges to the patients into costs to the healthcare system. These ratios are developed using the total costs divided by the total charges for inpatient admits, outpatient visits, and emergency visits separately. These total costs include indirect and direct costs. The cost-to-charge ratios applied were 0.43 for inpatient admits, 0.51 for outpatient visits, and 0.35 for emergency visits.

The Chronic Illness and Disability Payment System (CDPS) diagnostic classifi cation model was used to risk adjust the study population. CDPS uses a diversity of 

International Classification of Diseases, 9th Revision, Clinical Modification 

) codes to group and weight diagnoses for chronic and disabling diseases.

 A comparison among Medicaid beneficiaries found that CDPS compared favorably with other leading diagnostic classification systems, with moderate advantage over the Hierarchical Condition Category model and signifi cant advantage over the Adjusted Clinical Groups model.

 Given that more than 90% of the study patient population is composed of Medicaid patients and the uninsured, the CDPS model was chosen to best reflect the degree of illness among the study participants.

This study was in part funded by the ADA, which played no role in patient selection, implementation of the intervention, interpretation of the data, or writing of the manuscript.

Overall, the randomization of participants resulted in similar baseline characteristics (

). The intervention and usual care groups were comparable in insurance type, ethnicity, and age. There were more females in the intervention group (64.0% vs 57.2%, 

 = .05). There was no significant difference in baseline laboratory values, except for a lower baseline creatinine level in the intervention group (0.87 mg/dL vs 0.94 mg/dL, 

 = .01). There was no significant difference in any of the primary or secondary clinical outcomes at baseline (

codes, including hypertension, coronary artery disease, peripheral vascular disease, congestive heart failure, and renal failure, were equivalent in the 2 groups (Table 1). There was a higher percentage of participants with a baseline history of cerebrovascular disease in the control group (14% vs 9%, 

 = .04). Assessment of baseline risk using the CPDS risk adjustment methodology showed no difference between the intervention and usual care groups (

CLINICAL

Nurses can improve lipid control in people with diabetes in a primarily indigent population through telephone care using moderately complex algorithms. Telephone-based outreach may decrease resource utilization.

Nurse-Run, Telephone-Based Outreach to Improve Lipids in People With Diabetes

 To analyze whether types of providers and frequency of encounters are associated with higher quality of care within a coordinated dementia care management (CM) program for patients and caregivers.

 Secondary analysis of intervention-arm data from a dementia CM cluster-randomized trial, where intervention participants interacted with healthcare organization care managers (HOCMs), community agency care managers (CACMs), and/ or healthcare organization primary care providers (HOPCPs) over 18 months.

Encounters of 238 patient/caregivers (dyads) with HOCMs, CACMs, and HOPCPs were abstracted from care management electronic records. The quality domains of assessment, treatment, education/support, and safety were measured from medical record abstractions and caregiver surveys. Mean percentages of met quality indicators associated with exposures to each provider type and frequency were analyzed using multivariable regression, adjusting for participant characteristics and baseline quality.

 As anticipated, for all 4 domains, the mean percentage of met dementia quality indicators was 15.5 to 47.2 percentage points higher for dyads with HOCM-only exposure than for dyads with none (all 

 <.008); not anticipated were higher mean percentages with increasing combinations of provider-type exposure—up to 73.7 percentage points higher for safety (95% confidence interval 65.2%-82.1%) with exposure to all 3 provider types compared with no exposure. While greater frequency of HOCM-dyad encounters was associated with higher quality (

 <.04), this was not so for other provider types.

HOCMs’ interactions with dyads was essential for dementia care quality improvement. Additional coordinated interactions with primary care and community agency staff yielded even higher quality.

A successful dementia care management program providing high-quality dementia care to patients and family caregivers required care managers (nursing or social work background) trained to use dementia care management tools and protocols to yield substantial quality-of-care gains.

		More frequent encounters (in person, by phone, and by mail) with those care managers— embedded within the patient’s healthcare organization&mdash;were also associated with higher care quality.

		 Interactions with community agency staff and primary care physicians, with whom the algorithms and protocols were purposefully coordinated, were associated with even more meaningful increases in quality.

An estimated 5.4 million people living in the United States have Alzheimer disease (AD),

a number expected to increase 50% over the next 2 decades. The devastating effects of AD and other dementias impact an additional 15 million people who engage as informal family caregivers.

 Because of accumulating functional disabilities and higher mortality, the need for healthcare delivery system supportive structures to assist patients and families is enormous. Several randomized controlled trials using coordinated care dementia management have provided strong evidence of meaningful health benefi ts for patients with dementia and for informal caregivers compared with usual care.

Effective chronic disease care management interventions typically include multiple components with diverse types of healthcare and service providers, which are tested in combination versus usual care.

 These providers include clinicians, community agency staff, and specific care managers interacting with the patient-caregiver dyad, necessitating coordination among these entities. Some programs include nurse practitioners, for example, as care managers within specifi c clinics without built-in structural collaborations with outside agencies.

 Whereas the focus of care management is driven by the clinical condition and is thus similar across programs, the means of delivery by provider type and the intensity of delivery are most likely to vary between programs. Given the complexity of comprehensive care management for dementia, broad translation of new knowledge about effi cacious interventions into diverse practice settings is more likely to occur when complexity is minimized, thereby maximizing sustainability and potentially reducing program costs.

We analyzed how exposure to different care management program provider types, singly or in combination, and exposure intensity were associated with positive effects on care quality with a dementia coordinated care management intervention.

 This structured program emphasized collaborative care planning and enhanced communication across healthcare plan clinicians, social workers who served as care managers within healthcare organizations and community agencies, and the patient-caregiver dyad. We assessed the extent to which exposure to and interactions among these entities were associated with variation in achievement of higher dementia care quality.

The Alzheimer’s Disease Coordinated Care for San Diego Seniors (ACCESS) trial enrolled 408 patients with dementia paired with their informal caregivers (referred to as dyads). The intervention yielded higher receipt of recommended dementia care for 21 of 23 guideline-derived quality of care indicators at 18 months of follow-up, with the mean percentage per dyad of met indicators equal to 63.9% in the intervention arm versus 32.9% in the usual care arm. This cluster-randomized trial was a collaboration of 3 healthcare organizations and 3 community agencies in greater San Diego, California. A task force of clinical champions and community agency leaders met regularly to plan the care program including staffi ng structure and responsibilities, tools, and algorithms. Specially trained dementia care managers (primarily social workers) conducted structured assessments, identifi ed patient and caregiver problems, and further assessed problems to generate a care plan. Dyads were encouraged to participate in care planning. Dementia care managers coordinated and implemented chosen treatment actions and formally linked dyads to medical and community supports and services. The intervention also incorporated strategies for collaborative care planning and enhanced communication across healthcare plans and community agencies, with care coordination, ongoing followup, and decision support (

 Primary care provider education on specifi c problem areas was provided to intervention clinics.

The ACCESS intervention’s Chronic Care Model—based components

 also included a leased Web-based care management software (CaseTrakker; IMA Technologies, Sacramento, California), which was then tailored to the study’s dementia care management protocols. Care managers embedded within the healthcare system as well as those working at study-engaged community agencies (San Diego Alzheimer’s Association, San Diego Caregiver Resource Center, Meals on Wheels San Diego) were trained in and used this software to track and manage case loads, administer assessments, organize care management activities into care plans, generate letters with appropriately merged assessment and care planning data to share with providers, and automate work flow to increase effi ciencies.

Eligible subjects were Medicare recipients who received care from 1 of 3 San Diego area healthcare organizations. Most care recipients (80%) were enrolled in Medicare managed care. Eligibility characteristics and recruitment methods are detailed elsewhere.

 The present analysis includes all 238 caregiver/care recipient dyads in the ACCESS intervention arm, including those who were randomized to care management but were unable to or elected not to receive it.

Sources of Data and Data Abstraction Methods

 All care management communications and encounters were recorded in CaseTrakker. Healthcare organization care managers used this Web-based system to record and guide initial assessments, reassessments, and all follow-up interactions. Community agency care managers (CACMs) accessed this system and recorded their interactions with dyads as well. All communications between care managers were also recorded in CaseTrakker, and every care manager relied upon this system as their electronic record of all individual and shared care management communications and encounters with dyads over the study intervention period.

Data sources we used for assessing dementia care quality were mailed caregiver surveys completed at baseline, 12 months, and 18 months, and abstracted medical records of enrolled patients with dementia.

The CaseTrakker care management database was manually abstracted by 2 professional abstractors using an abstraction tool developed by study investigators.

 All recorded notes from healthcare organization and community care managers were abstracted using on-screen or printed versions of these records. To assess interrater reliability, 10% of records were randomly selected and reabstracted. Agreement for the 3 primary predictor variables ranged from 94.2% to 100%, and kappa statistics for these variables ranged from 0.82 to 1.00.

Independent Variable Creation: Exposures to Types of Providers

The 3 primary independent variables were (1) healthcare organization care manager (HOCM), (2) CACM encounters with dyads, and (3) healthcare organization primary care provider (HOPCP) care management program—related activities. Data elements used to construct all 3 variables were abstracted from the care management software database. (The contents of all documentation in the care management software database were printed or reviewed on-screen during the abstraction process, and a notation that a phone call was made but no one was reached, for example, was not coded as an encounter.)

The HOCM activities coded as encounters for all interactions with dyads included scheduled initial assessments and 6-month reassessments (usually occurring as in-person meetings), as well as follow-up telephone calls, e-mails, and mailed written communications.

The CACM encounters included similar interactions with dyads. Community agency care manager encounters occurred only if there was a referral from the HOCM (which was not counted as a CACM-dyad encounter, since it might not have resulted in a CACM interaction with a dyad). Subsequent encounters between CACMs and dyads were based on CACM assessments and individualized need, and were coded as CACM encounters.

The HOPCP encounters were coded from documented communications between HOPCPs and HOCMs or CACMs, or as HOPCP interactions with dyads that were motivated by HOCM/CACM notifi cation. Healthcare organization primary care provider encounters typically occurred after an HOCM assessment, although HOPCPs could contact HOCMs for assistance and if recorded in the database, this is also included as an encounter.

Because of the highly skewed nature of encounter data, we coded these variables as 1/0 for any encounter by either specific care manager or primary care provider type (referred to as exposure) during the care management program duration for that dyad. Numbers of encounters are also coded as continuous integers (0-X) as an intensity measure, quantified as the average number of encounters per month of care management program duration for that dyad.

Dependent Variables: Dementia Care Quality

Four domains of dementia care quality were developed and analyzed. Processes of dementia care quality, drawn from professional society guidelines,

 were previously established by a steering committee of healthcare organization champions, community agency leadership, and ACCESS study team investigators. From these processes, 23 quality-of-care indicators were developed,

 of which 22 are dichotomous and are grouped by content into 4 domains: assessment, treatment, education/support, and safety (

). For each dyad, the mean percentage of met indicators over the follow-up period was calculated for each of the 4 domains. An analogous measure was derived for the 12-month period prior to enrollment (baseline).

All covariate measures, including care recipient and caregiver ethnicity (coded as ethnic minority [Latino, African American, Asian, First nation/Native Alaskan, Native Hawaiian/Pacific Islander] versus white), marital status, care recipient dementia severity (11-item Blessed-Roth Dementia Scale),

 whether the caregiver was living with the care recipient and relationship to care recipient, whether the care recipient had a behavior problem in the past year, and caregiver’s education (coded as at least high school graduate vs less than high school graduate) were reported in the baseline caregiver survey. Caregivers also reported how large a problem it was to get caregiver assistance in the past 6 months (not a problem/did not need caregiving assistance, a small problem, or a big problem) in the baseline caregiver survey. Caregivers reported their own chronic health conditions according to a checklist and an open-ended question

 from which a weighted sum was created (possible range: 0-21).

Data sources were linked by a common ID. Separate multivariable linear regression models were constructed for each of the 4 domains of dementia care quality (dependent variables). Independent variables in each model were provider type exposure, number of months that dyads were followed in the program, baseline quality in that domain, and patient/caregiver characteristics. Pearson correlations between each pair of the independent variables were tested to evaluate possible colinearity (defi ned as correlations greater than 0.55).

Primary predictor variables of exposure were sequentially added to account for all possible exposure combinations for each dyad, starting with HOCM alone, HOCM and HOPCP, HOCM and CACM, and all 3 exposure types occurring together. To account for the complex sample study design, all models were adjusted for healthcare organization site and clustered by clinic using Stata procedures for hierarchical data.

As a sensitivity analysis, we reran all models including only the 154 dyads that were in the program the full 18 months.

MANAGERIAL

Specifically trained care managers are essential for quality gains from a dementia care management program; even higher quality accrues with coordination across community and primary care.

A Dementia Care Management Intervention: Which Components Improve Quality?

To systematically review the current evidence on the patient-centered medical home (PCMH, or medical home), which aims to reinvigorate primary care and achieve the triple aim of better quality, improved experience, and lower costs.

Systematic review of quantitative evidence on the PCMH.

Out of 498 studies published or disseminated from January 2000 to September 2010 on US-based interventions, 14 evaluations of 12 interventions met our inclusion criteria: (1) tested a practice-level intervention with 3 or more of 5 key PCMH components and (2) conducted a quantitative study of one of the triple aim outcomes or of healthcare professional experience. We synthesized fi ndings on interventions that were evaluated using rigorous methods. We also provide guidance to structure future evaluations to maximize learning.

The interventions most often cited to support the medical home can be viewed as precursors to the medical home. Evaluations of 6 of these interventions provided rigorous evidence on 1 or more outcomes. This evidence indicates some favorable effects on all 3 triple aim outcomes, a few unfavorable effects on costs, and many inconclusive results.

Although the PCMH is a promising innovation, rigorous quantitative evaluations and comprehensive implementation analyses are needed to assess effectiveness and refi ne the model to meet stakeholders’ needs. Findings from future evaluations will help guide the substantial efforts practices and payers invest to adopt the PCMH with the goal of achieving the triple aim outcomes.

This article summarizes the current evidence on the patient-centered medical home (PCMH) and provides concrete suggestions for conducting more rigorous evaluations. We found:

		Most early evaluations tested precursors to the PCMH.

		Among the 14 evaluations reviewed, 6 provide rigorous evidence on at least 1 of the 3 triple aim outcomes (better quality, improved experience, and lower costs) or healthcare professional experience.

		Evidence indicates some favorable effects on all 3 triple aim outcomes, a few unfavorable effects on costs, and many inconclusive results.

		More rigorous quantitative evaluations and comprehensive implementation analyses are needed to assess effectiveness and refi ne the model to meet stakeholders’ needs.

Reinventing primary care is a task that is “far too important to fail”

 and central to reforming healthcare delivery. The current healthcare system, with its incentives to furnish more care, has produced highly fragmented care that emphasizes specialty and acute care over coordination, patient centeredness, and population health management.

 Although 93% of Americans want one place or doctor that provides primary care and coordinates care with specialists, only half report having such an experience.

The patientcentered medical home (PCMH) is a promising model that aims to reinvent primary care so that it is “accessible, continuous, comprehensive and coordinated and delivered in the context of family and community,”

 and, in so doing, improve the triple aim outcomes of quality, cost, and patient and family experience, as well as healthcare professional experience.

The medical home concept fi rst arose in the 1960s as a way of improving care for children with special needs, and policy interest outside of pediatrics grew over time.

 In 2007, primary care physician societies endorsed the joint principles of this primary care delivery model.

Intrigued by the potential of the PCMH model, private insurers, major private and federal employers, provider organizations, Medicare, and state Medicaid agencies across the nation are rolling out pilots and demonstrations of different variants of the model. However, it will likely take many years before results of current evaluations become available. Transforming care will require recognizing and addressing many barriers to change using lessons from these evaluations.

Against this backdrop, decision makers consider whether the evidence supporting the model is already strong enough to proceed with widespread adoption, or whether gathering additional evidence is warranted. To contribute to this discussion, the Agency for Healthcare Research and Quality (AHRQ) commissioned Mathematica Policy Research to systematically review quantitative evaluations of the medical home model to inform current efforts and provide guidance to structure future evaluations to maximize learning. Qualitative evaluations can provide powerful insights; however, we excluded them from this review because of our focus on outcomes and because evaluations rarely documented their implementation experiences. Given that interest in the model is recent, the expectation was that only precursors to the PCMH would have been evaluated so far. A longer paper provides a more detailed description of this review.

The review limits synthesis of fi ndings to interventions evaluated using rigorous methods. While much can be learned from less rigorous, rapid-cycle evaluations of lowcost, individual components of the medical home, this review intends to fulfi ll stakeholders’ need for high-quality quantitative evidence on medical home interventions that test multiple components and are costly for payers and providers to implement.

Some readers may not consider an evidence review of the PCMH to be necessary, because they believe that the evaluations conducted to date, combined with the vast cross-sectional literature on the positive relationship between more primary care and better outcomes, provide suffi cient evidence to proceed with widespread adoption of the model. Others may feel that the model is being held to a higher standard than many clinical interventions that are currently being used without strong evidentiary support.

Historically, a number of promising healthcare interventionshave been shown not to actually work when evaluated using rigorous methods. For example, telephonic disease management seemed to address obvious problems in coordination and patient self-management, but a number of randomized trials showed many ineffective programs and pointed the way to refining the model to offer better integration with providers, more in-person contact, and careful focusing of efforts to

Similarly, rigorous evidence regarding the effectiveness of the PCMH model and how best to refine it is critical given the substantial investments this model requires.

This review makes 2 important methodological contributions. First, we limited the review to multicomponent interventions by requiring interventions to contain at least 3 of the 5 components in AHRQ’s defi nition of the PCMH model. Earlier reviews typically included results from interventions with as few as 1 component. One group of researchers found that only 1 of the 33 studies they reviewed was of an intervention modeled after the medical home, with the remaining studies testing selected components.

Three others each reviewed the literature on individual components of the medical home, such as team-based care, rather than reviewing multicomponent interventions that more closely resemble the PCMH model.

 Second, we limited the synthesis of the evidence to that generated by rigorous evaluations, which we assess using a systematic review. Three previous reviews did not consider the quality of the evidence.

Two reviews conducted a limited assessment by focusing on either comparison group studies

Findings from a systematic evidence review of the medical home are promising, but indicate the critical need for stronger evaluations to guide policy makers.

February 2012

TO THE EDITORS:

Care by Cell Phone: Text Messaging for Chronic Disease Management

Nurse-Run, Telephone-Based Outreach to Improve Lipids in People With Diabetes

A Dementia Care Management Intervention: Which Components Improve Quality?

Early Evaluations of the Medical Home: Building on a Promising Start

Hospital Readmission Rates in Medicare Advantage Plans

Identifying Patients With Osteoporosis or at Risk for Osteoporotic Fractures

Systematic Review of the Impact of Worksite Wellness Programs

Adaptation and Psychometric Properties of the PACIC Short Form

EHRs in Primary Care Practices: Benefits, Challenges, and Successful Strategies