 To examine the relationship between glycated hemoglobin (A1C) levels and the number of days spent waiting for primary care appointments.

 Retrospective observational study that relied on Department of Veterans Affairs (VA) utilization data and Medicare claims data from 2001 to 2003. The outcome was A1C levels. The main explanatory variable of interest was facility-level primary care wait times measured in days.

Heckman selection models simultaneously predicted the presence of an A1C value and its level. Models were risk adjusted for prior individual health status. Separate models were estimated on the entire sample and on subsamples stratified by baseline A1C levels.

 Veterans who visited VA facilities with wait times of longer than 32.5 days had small significant increases in A1C levels of 0.14 percentage point for the whole sample, 0.07 percentage point for patients with baseline A1C levels less than 7%, 0.11 percentage point for patients with baseline A1C levels between 7% and 8%, and 0.18 percentage point for patients with baseline A1C levels greater than 8%.

Decreasing wait times has the potential to reduce A1C levels by 0.18 percentage point for patients with baseline A1C levels exceeding 8%. This effect is roughly one-third of what is achieved with the most successful existing quality improvement strategies. Ensuring timely access to outpatient care could be an important addition to future diabetes care quality improvement programs.

	Diabetes care quality improvement strategies have had limited success in lowering glycated hemoglobin (A1C) levels, with annual decreases of 0.50 percentage point in A1C levels considered successful. An overlooked element in quality improvement is timely access to healthcare.

		This study finds that longer wait times for primary care are associated with small increases in A1C levels (0.18 percentage point for patients with poor baseline control).

		The size of the effect from wait times is roughly one-third of the effect found for the most successful quality improvement strategies.

		Healthcare managers should consider adding to quality improvement programs tested interventions that ensure timely access to care.

	Diabetes mellitus is a prevalent costly illness that significantly increases the risk of poor health outcomes and increased healthcare costs.

 An essential focus of diabetes management is maintaining glycemic control.

 Despite accepted evidencebased guidelines and consensus in the medical community about appropriate processes of care, many patients with diabetes do not have optimal glycemic control.

	The uneven implementation of processes of care in diabetes management has led to numerous quality improvement (QI) interventions. These interventions can target patients (eg, patient education) and providers (eg, physician reminders) or can reorganize the delivery of healthcare services (eg, case management use of pharmacists to adjust medication).

Evaluations of these interventions have found that they succeed at improving the process of diabetes care (eg, measuring blood glucose levels) but that the effect of these interventions on glycated hemoglobin (A1C) levels is limited.

	A key to glycemic control that may be overlooked in current diabetes QI programs is the timeliness of access to care. Successful management of diabetes requires patients to consistently access care in a timely manner. For example, the progressive nature of diabetes means that glycemic control will likely deteriorate over time and that changes in glucose-lowering medication will be needed. Similarly, acute illnesses may destabilize a patient’s glycemic control.

Therefore, patients who consistently have longer wait times for appointments may have poorer glycemic control and outcomes compared with patients who have better access to healthcare services.

	This study is the first to date to examine the relationship between wait time for primary care services and glycemic control in a nationwide sample by capitalizing on unique data available through the Department of Veterans Affairs (VA). Distinct from other health payers and healthcare systems, the VA automatically monitors wait times for appointments nationwide. Geographic variations in the demand for VA care or differences between VA facilities in the implementation of policies and programs aimed at managing excess  demand result in varying wait times across VA facilities.

 We exploited this variation in wait times to examine the effect of wait time for healthcare services on glycemic control   and hypothesize that patients who visit VA facilities with longer wait times may have poorer glycemic control.

	This study used administrative data from the VA and from Medicare. The sample was extracted from the VA Diabetes Epidemiology Cohorts database, a registry of all VA patients nationwide with diabetes mellitus since 1998. The criteria for identifying patients with diabetes included a prescription for diabetes medication in the current year or at least 2  diabetes codes for inpatient or outpatient visits (VA and Medicare) over a 2-year period.

	Our sample consisted of veterans diagnosed as having diabetes before 2001. Outcomes were measured in 2002 and 2003, with 2001 used as a baseline year for risk adjustment (see “Data Availability” section in 

 guidelines at the time of the study called for A1C levels to be tested biannually for individuals with glycemic control and quarterly for individuals without glycemic control. We split the outcome period into the following 4 six-month observation periods: (1) the first half of 2002, (2) the second half of 2002, (3) the first half of 2003, and (4) the second half of 2003.

	To ensure complete utilization, laboratory, and diagnosis data, we required the sample to be VA users and Medicare eligible. The most important criterion for entering the sample was that individuals relied exclusively on the VA for outpatient physician visits in 2001. Following methods developed by Gardner et al, we counted outpatient physician visits in comparable ways among the VA utilization and Medicare claims data.

Veterans who had VA outpatient physician visits and no Medicare physician visits in the baseline year were eligible for the sample. We confirmed that these individuals continued to rely heavily on the VA during the outcome period (

). This was important because we needed complete A1C laboratory values, and Medicare claims do not provide laboratory values.

 A second criterion for entering the sample was whether we had complete claims data for baseline risk adjustment. Ineligible individuals included those without baseline A1C levels for stratification or those enrolled in Medicare health maintenance organizations in 2001 (incomplete diagnosis codes for risk adjustment).

	We further excluded individuals who died during the outcome period (because of changes in A1C levels right before death), individuals with missing race/ethnicity data, and individuals in a hospital or nursing home during the entire outcome period. The final sample size totaled 84,244 individuals (

	The main explanatory variable of interest was the mean wait time until the next available primary care appointment for patients new to the primary care clinic at a VA facility. This was an overall measure of congestion at the facility, as VA managers assume that almost all new patients want the next available appointment.

 demonstrated that wait times based on services that an individual actually used are inappropriate for studying the effect of wait times on outcomes. Unobserved individual health status affects individual wait times and outcomes because sicker patients are triaged to receive care faster.

	Because we cannot use the individual wait time, we use a proxy measure. Our measure is a facility-level mean wait time that is exogenous to the individual and is similar to a measure used in previous research.

 Each individual’s zip code was obtained from the Medicare denominator file in 2002 and 2003 and was used to assign him or her to the nearest VA facility. All individuals who were closest to the same VA facility were assigned the same exogenous wait time corresponding to that facility. The limitations of relying on this proxy wait time measure, including the possibility of biasing results toward the null, are considered in the “Discussion” section.

	A key aspect of our study design was to include dummy variables (fixed effects) for each facility in the models. The facility fixed-effects specification removed between-facility variation in wait times and outcomes, alleviating concerns about case-mix differences across facilities and controlling for time-invariant facility quality.

	The remaining variation in wait times and outcomes came from within-facility changes over time, so individual patients served as their own controls. In effect, we compared the A1C level of an individual in one observation period with the A1C level of the same individual in other observation periods, which eliminated concerns about case-mix differences between facilities.

	Similarly, facility fixed effects control for all aspects of facility quality that remain constant over time. For example, facilities with managerial inefficiencies may provide poor quality of care and have longer wait times. The poorer quality of care also increases the risk of poorer health outcomes independent of wait times.

 Facility fixed effects eliminated this potential confounding.

	Finally, we included dummy variables to control for the seasonality of A1C levels and any overall increase or decrease in A1C levels over time. This statistical design, featuring a predetermined cohort of patients and time and facility fixed effects, means that any estimated relationship between wait time and A1C level was identified exclusively by within-facility variations over time that were independent of national trends.

 Risk Adjustment and Other Explanatory Variables

	Models were risk adjusted to control for observable differences in prior individual health status. Explanatory variables included age, sex, race/ethnicity, and diagnosis-based variables. Other explanatory variables included distance to VA care and VA priority status (1-3 vs >3). Longer driving distances have been found to be associated with poorer glycemic control,

 and veterans with a service-connected disability (priority status 1-3) receive priority access and likely experience shorter wait times.

	We extracted diagnosis codes from all inpatient and outpatient encounters financed by the VA and Medicare during the 2001 baseline period and used the 

	Classification of Diseases, Ninth Revision, Clinical Modification

) diagnosis codes listed by Elixhauser et al

to define 28 comorbidity indicator variables, which included a wide variety of physical and mental conditions.

	The Diabetes Complications Severity Index developed by Young et al

 was used to control for diabetes severity. This index included measures of complications from retinopathy, nephropathy, neuropathy, cerebrovascular disease, cardiovascular disease, peripheral vascular disease, and metabolic disease. The

 codes from VA and Medicare  outpatient and inpatient utilization and claims files for 2001 were used to determine each individual’s Diabetes Complications Severity Index.

	Data were analyzed using commercially available statistical software (STATA 10.0; StataCorp LP, College Station, TX). We modeled the mean 6-month A1C level during each observation period. The mean wait time for the previous 6 months predicted the A1C level in the current 6-month period. Therefore, each individual contributed up to 3 outcome observations because his or her first 6-month period had no lagged data available. Standard errors were clustered on individuals to account for the lack of independence between observations from the same individual.

	Patients in a hospital or nursing home during the wait time measurement period should not be affected by outpatient wait times. Therefore, we censored observation periods if the veteran was in a hospital or nursing home for all 6 months during the wait time measurement period.

	Despite using 6-month observation periods to maximize the availability of A1C data, 34% of A1C values during the outcome period were missing. As discussed in Appendices A and 

, after considering possible imputation techniques, we treated these observations as censored using a 2-stage selection model developed by Heckman.

 The first stage of the Heckman model used a probit to explain whether or not an A1C level was observed, and the second stage used linear regression analysis to explain A1C levels for observations that had recorded values. The 2 stages were jointly estimated so that the missing observations were accounted for in the second stage. This simultaneous equations approach explicitly modeled the correlation between unobservable factors in the first and second stages. The necessity of the Heckman model was confirmed with a significant Wald statistic, which tested whether this correlation was 0 and indicated that common unobservables affected both censoring and the outcomes (

). We estimated separate Heckman  models using the full sample and then stratified by baseline A1C level with a linear wait time specification, a quintile wait time specification, and a dichotomized wait time specification. 

 give complete results from both stages of the Heckman model for the dichotomized wait time specification.

Articles

WEB EXCLUSIVE

Longer primary care wait times were associated with small increases in glycated hemoglobin levels. Diabetes care quality improvement strategies should consider timely access to care.

Outpatient Wait Time and Diabetes Care Quality Improvement

 To determine the frequency of and reasons for medically unnecessary hospital days.

 We developed an online survey to pro-spectively collect data on hospitalists' and discharge planners' perceived delays in treatment or discharge for patients on their general medicine services. Over a 2-month period, hospitalists and discharge planners completed a daily online survey. 

 We collected data on 3574 patient-days from our hospitalists and data on 2502 patient days from our discharge planners. Among the hospitalists'responses, 395 patient-days (11%) were thought to be unnecessary. Among the discharge planners' responses, only 186 patient-days (7%) were thought to be unnecessary. The hospitalists believed that the most common reason for discharge delay was lack of extended care facility availability (111 patient-days [28%]), followed by patient or family reasons (62 patient-days [15%]), procedure delays (62 patient-days [15%]), and test scheduling delays (52 patient-days [13%]). The discharge planners' data were similar.

 More than 10% of hospital days were reported by our hospitalists to be unnecessary at this academic medical center. Major reasons were lack of extended care facility availability, patient or family reasons, procedure delays, and test scheduling delays. A simple survey instrument to assess perceived delays in the hospital may provide real-time information to initiate improvement changes to reduce excess hospitalization days.

	More than 10% of hospital days were reported to be unnecessary at this academic medical center.

		This figure is in alignment with previously reported studies.

		Hospitalists working independently or with house officers reported similar rates of excess hospitalization days. 

		Because hospitals are employing more hospitalists, the use of a simple real-time data collection tool to identify the frequency of and reasons for excess hospitalization days may focus improvement efforts in these highly occupied organizations.

	Excess hospitalization days are of major concern to patients, payers, hospitals, and healthcare providers. Implications of excessive length of stay include the following: (1) greater costs, (2) potentially increased iatrogenic illness, and (3) high hospital occupancy with resultant loss of efficiency and access. To address these concerns, hospitals have developed support systems that include discharge planning services, home care services, and hospitalist services.

	According to the Society of Hospital Medicine,

 the hospitalist field has grown rapidly, with more than 30,000 hospitalists now in practice. At academic medical centers (AMCs) in particular, many programs have expanded to include the following 2 types of hospitalist services: (1) the traditional resident team (RT), where a hospitalist is the supervising attending physician, and (2) a non—house staff hospitalist team (HT), where the hospitalist works independently to provide direct patient care.

 The growth of hospitalist programs in AMCs has been largely attributed to (1) offloading the volume of patients on RTs limited by work hour rules of the Accreditation Council on Graduate Medical Education and (2) filling an overall increasing demand for hospital care services.

 published a study on the epidemiology of unnecessary hospital days in an AMC. Subsequent studies

 have measured and classified excess hospitalization days to assess the potential effect on healthcare systems. These studies found 10% to 20% of hospital days to be unnecessary. To determine the frequency of and most common reasons for delays on the general medicine services at our AMC, we asked our hospitalists and our discharge planners to complete a daily online survey. To our knowledge, this is the first study comparing the views of hospitalists (on RT and HT services) and of discharge planners on excess hospitalization days.

	Our hospital is a tertiary care AMC with more than 600 adult beds and 34,000 annual discharges. The 39 hospitalists in our group staff almost 10,000 total discharges. Hospitalists serve as supervising attending physicians on RTs and see patients individually on HTs. Both teams provide hospital coverage 24 hours a day and 7 days a week. Each discharge planner works with 1 to 3 of these RT or HT services.

as guides, we developed a brief (<5 minute) online survey instrument that asked hospitalists and discharge planners whether any of their patients on the general medicine services experienced an excess hospitalization day. They could further categorize the delay as an intrahospital delay (related to an inhospital service not completed in a timely manner) or as a discharge day delay (prevented discharge of a patient who was otherwise medically ready for discharge). To facilitate completion of the voluntary surveys, we generalized the comprehensive categories of delay from previous studies to 4 intrahospital delay choices and 5 discharge day delay choices. The  respondents were restricted to a single response choice and could type in more detailed information as free text, or they could select “other” and write in a response. We identified  the hospitalists and their respective discharge planners working on the day shift for each day of the survey from a prearranged work schedule and sent daily e-mail messages asking them to independently complete the surveys from August 6 to October 1, 2008. The survey is given in the 

	The survey responses were the only data source utilized in analyzing our results. Descriptive statistics were used to assess the frequencies and categories of excess hospitalization days. 

2 Analyses were used to assess unadjusted bivariate comparisons between groups of interest with respect to reasons for the excess hospitalization days. Two-tailed a was set at .05. All analyses were performed using commercially available statistical software (STATA/IC 10.0; StataCorp LP, College Station, Texas).

	Hospitalists completed 65% of the potential total number of surveys, yielding data on 3574 patient-days, and discharge planners completed 63% of the potential total number of surveys, yielding data on 2502 patient-days. The hospitalists’ data included 1632 patient-days from the RT services and 1942 patient-days from the HT services. Among all hospitalist’ responses, 395 patient-days (11%) were believed to be unnecessary. The RT patients experienced 171 delay days (11%), while the HT patients experienced 224 delay days (12%). Further comparison between weekdays vs weekend days between the RT and HT services revealed no significant differences. Among all discharge planners’ responses, 186 patient-days (7%) were believed to be unnecessary, a statistically significant difference compared with the hospitalists’ responses (P <.01).

	Of 395 excess hospitalization days reported by our hospitalists, 224 (57%) were due to discharge day delays, and 171 (43%) were due to intrahospital delays. The most common reasons for excess hospitalization days were lack of extended care facility availability, patient or family reasons, procedure delays, and test scheduling delays. 

 lists the most commonly selected categories of delay as reported by our hospitalists and our discharge planners.

	Of 186 excess hospitalization days reported by our discharge planners, 145 (78%) were due to discharge day delays, and 41 (22%) were due to intrahospital delays. The discharge planners generally agreed with the hospitalists’ categorization of the most common reasons for excess hospitalization days (Table 1).

 Specific Categories of Intrahospital Delays

	As part of a postsurvey analysis of intrahospital delays, 2 hospitalist reviewers (ALH and RFP) manually reviewed each free-text response provided and totaled the number of days attributed to each type of delay (

). From the hospitalists’ perspectives, procedure delays accounted for most of the intrahospital delays, followed by consult delays. Among the procedure delays, radiology-related services accounted for more than half of the delay days. The discharge planners provided fewer specific reasons for delays but generally agreed with the hospitalists’ views on intrahospital delays.

	Hospitalists at our AMC perceived 11% excess hospitalization days, with no significant difference between the RT and HT services. Our discharge planners perceived 7% excess hospitalization days. Both hospitalists and discharge planners identified the following categories of discharge delay to be the most common: (1) lack of extended care facility availability, (2) patient or family reasons, (3) procedure delays, and (4) test scheduling delays.

	We note similar rates of excess hospitalization days across study locations and periods relative to previously published studies. Carey et al

 reported a 14% rate of unnecessary hospital days in their general medicine services over 2762 days, while Selker et al

 reported a 17% rate of unnecessary hospital days among general medicine and gastrointestinal patients over 7795 days. More recently, a study

 of hospitalized general pediatric patients identified 9% excess hospitalization days over 911 days.

	The hospitalist movement represents a dramatic evolution in healthcare, with 30,000 hospitalists now in practice.

 At most AMCs, where the house staff census remains fixed and the demand for hospital care increases, medicine hospitalist groups have expanded their presence to provide direct patient care.

 Resident teams have maintained the traditional structure of attending, resident, interns, and students. Attending-only hospitalist teams can theoretically run “leaner,” with improvements in efficiencies and work flow. However, our study shows no difference in the number of excess hospitalization days between these 2 types of general medicine services. When the categories of delays were classified as intrahospital delays vs discharge day delays, the rates were again similar between the 2 types of hospitalist services (38% on RTs and 47% on HTs for intrahospital delays vs 62% on RTs and 53% on HTs for discharge day delays). Despite speculation that direct attending—level care by hospitalist teams could run more efficiently and result in fewer excess hospitalization days, further studies are needed to determine how hospitalists’ daily work flow compares with that of house staff–run teams and whether these differences lead to greater efficiencies in patient care services. It may be that the lack of differences observed between the 2 types of hospitalist services in our facility reflects external factors beyond the control of primary hospitalists. As hospitals have engaged discharge planners, home care services, and hospitalists, the next logical approach may be to identify and work with specific hospital services such as stress tests, radiology studies, and gastrointestinal procedures, as well as to employ skilled nursing facilities to establish partnerships to expedite the transition of patients from the acute care facility. Providing real-time feedback to hospital administration and providers regarding specific reasons for excess hospitalization days may drive health system efficiency efforts to these selected areas identified by the hospitalists.

	Our analysis also revealed that among the top perceived reasons for excess hospitalization days was patient or family unreadiness. For many patients, the course of a hospital stay can be an intense experience and require ongoing care as they transition out of the acute care setting. Early discharge initiatives can prepare patients and families for the discharge process.

 Patients and their family members need to be engaged and actively involved in the discharge process, identifying needs and facilities even as early as at the time of admission.

	Excess hospitalization days raise concerns for patient safety, quality, and efficiency, but also herald significant financial costs to the healthcare system as a whole. Extrapolating our data over a year’s time yields 5500 excess hospitalization days (11% of 10,000 discharges staffed, with a mean length of stay of 5 days). Patients and healthcare practitioners and administrators should certainly appreciate the resultant cost burden. These resources could be better used to provide care to a broader patient population within the community.

	Our study also included the perspectives of discharge planners, who work closely with our hospitalists, and found that the discharge planners perceived a lower rate of excess hospitalization days. Closer evaluation of the survey responses revealed that our discharge planners perceived that a greater percentage of delays occurred after patients were  believed to be medically ready for discharge than our hospitalists (and vice versa, where the discharge planners thought that fewer delays were attributable to intrahospital delays compared with our hospitalists). Although we can only speculate as to the reasons for these differences in perception, we believe that contributing factors to this observation may include the following: (1) discharge planners, whose specialty is to primarily focus on the needs of patients after (or near the point where) they have been deemed ready for discharge, may have been biased to find more delays in this category, and (2) discharge planners in general do not review all the consultant records, nor do they speak with the diagnostic or therapeutic procedural departments to order tests or obtain results. Therefore, they may not always have had the information to determine if there was an intrahospital delay and could have underestimated this category of delays.

	Ongoing quality improvement initiatives to reduce the number of excess hospitalization days are proposed. A simple data collection tool can help identify the most common reasons for excess hospitalization days on a real-time basis, which in turn can be used to deploy countermeasures with appropriate hospital-based specialists and staff. In our organization, we expect to further explore the intrahospital delays as characterized in Table 2. By implicating procedural services with the largest proportion of excess hospitalization days,  valuable feedback can be provided to these departments to improve patient flow. Furthermore, although our data did not directly evaluate whether weekend services led to greater rates of excess hospitalization days, the literature suggests that weekend service restrictions may contribute to inappropriate delays and that weekend availability of services should continue to be monitored for perceived delays.

This type of real-time information regarding specific hospital services may be helpful to hospital administration focusing on efficiency of clinical operations.

	There are inherent limitations to our quality improvement survey. First, our data were collected from a single AMC, and the results may not be generalizable. Furthermore, given that our study population included patients admitted to the general medicine services, the results may not be applicable to other clinical services such as pediatrics and surgery. Second, we had less than 100% compliance with completion of the survey. In particular, if there was some sort of systematic lack of response in which some hospitalists may have responded to the survey at a much lower rate than other hospitalists, there may be overrepresentation or underrepresentation biases introduced within the results. Because we collected data on more than 3500 patient-days from our hospitalists and more than 2500 patient-days from our discharge planners, we believe that the data are fairly representative of our AMC during the study period. Third, we identified a difference in the perception of excess hospitalization days between the hospitalists vs the discharge planners. This could be attributed to our survey process in which discharge planners did not complete surveys on weekends, and review of the data showed that some discharge planners reported an unspecified number of multiple delay days. Given the lack of clarity, we input these data points as a single delay day, which may have underestimated some of the discharge planners’ perceptions. Fourth, weekend days may be times of reduced efficiency, with at least 1 study

 noting resultant excess hospitalization days on weekends, and there are likely other forces (such as staffing and hours of operation) that can lead to excess hospitalization days for a hospital. Fifth, given that our survey allowed for respondents to select only a single response, it is unclear how respondents may have dealt with competing reasons for a discharge delay, which in turn may have affected some of our categorization of results. Furthermore, because the primary intent of our evaluation was to determine the frequency of delay, we did not ask our discharge planners or our hospitalists to include patient-specific information, and we were unable to perform any sort of interrater comparative statistical analysis.

	In conclusion, more than 10% of hospital days on the general medicine services were found to be unnecessary at our AMC. A simple-to-use data collection survey can help staff recognize excessive hospitalization days in real time and quickly identify the reasons for delay. Hospitalists could work cooperatively with discharge planners to gather data prospectively and report their findings on causes of delay. This may be relevant to hospitalist groups, who care for an increasing proportion of inpatients. Moreover, such data should be of interest to hospital administrators, as well as to payers of this nation’s increasingly costly inpatient care.

We assessed the frequency of and reasons for medically unnecessary hospital days, which affect patients, payers, hospitals, and healthcare providers.

Excess Hospitalization Days in an Academic Medical Center: Perceptions of Hospitalists and Discharge Planners

	Health insurers in India currently face many challenges, including poor consumer awareness, strict regulations, and inefficient business practices. They operate under a combination of stifling administrative costs and high medical expense ratios which have ensured that insurers operate under steep losses. External factors (eg, onerous regulations, lack of standards, high claims payouts) and internal factors (eg, high administrative costs, dependence on indemnity models that cover inpatient treatment costs only) have forced the health insurance industry into a regressive spiral. To overcome these challenges, health insurers need to innovate in their product offerings and tighten their existing processes and cost structures. But as a long-term strategy, it is imperative that health insurers deploy managed care concepts, which will go a long way toward addressing the systemic issues in the current operational models of health plans.

	Managed care firms can play a key role in India's emerging health insurance sector and help strengthen the country's overall healthcare effectiveness.

		The health insurance industry in India today is loss making and does not use any managed care techniques.

		Simple and innovative managed care techniques can go a long way toward addressing many of the industry challenges.

		Because there is a need for expertise on policy formulation at the industry level, US managed care firms have the opportunity to play a key role in this emerging sector.

India offers a wide variety of healthcare services to its population. On one hand there are the advanced hospitals and diagnostic centers in urban areas, whereas the rural areas depend significantly on government health centers. Between these 2 extremes there are government hospitals, private hospitals, private practitioners, dispensaries, and clinics (including Indian traditional medicine systems). Despite health being a major government concern, the majority of India’s health infrastructure is in the private sector and more than 70% of healthcare expenses are met by consumers and not the government (

). Given this unbalanced mix of healthcare funding and a per capita income of less than $600, medical costs are unaffordable for a majority of India’s population.

To address healthcare affordability, commercial health insurance was introduced in India by the government-owned general insurers as a standardized annual indemnity product in the mid-1980s, with a view to providing alternate financing options to members and helping them access better quality healthcare. Today, with the increased liberalization of the insurance industry, many private players have entered the health insurance market, resulting in increased awareness and growth of commercial health insurance (

).  Currently, around 20% of the total population is covered under health insurance schemes, with the majority covered under either government or employer programs; regular commercial health insurance has less than 2% penetration.

CHARACTERISTICS OF HEALTH INSURANCE IN INDIA

Despite the liberalized environment and increased presence of private insurers, the health insurance industry has been characterized by high government control and lack of innovation, which has stunted the overall growth. The sector has not evolved significantly enough to play a major role in the country’s health policy. Some of the systemic characteristics of the industry are listed here.

Health insurance is governed by the Insurance Regulatory Development Authority of India, which is the apex body for regulating all insurance firms (life, property, casualty, and health). Currently, health insurance schemes are mostly provided by life insurance and non—life insurance companies. Only 3 firms have recently started business as stand-alone health insurers. Hence most of the regulations covering health insurance flow down from the regulations for the life insurance and non–life insurance sectors, which include an approximately $25 million capital requirement to start a health insurance business. Moreover, current industry regulations prevent stand-alone health insurers from tying up with banks as a distribution channel. In India, banks have the maximum reach in terms of branches and geographical coverage, and are the primary channel for distributing all financial and insurance products.

 It is hence imperative that health insurance industry regulations get calibrated to reflect the business challenges.

Health insurance in India is sponsored by both government and private insurers. It was first marketed by the governmentowned insurers as a standardized annual indemnity product in the mid-1980s, and the premiums were largely based on the annual limit chosen and the age of the prospect. Today, more than 40 insurance firms (including life, general, and standalone health insurers) provide health insurance products.

One of the biggest drawbacks is the lack of standard terminology and protocol in treatment and billing of common illnesses. In many instances, different hospitals across the country use different terms and follow different treatment protocols and charges for treating the same medical condition.

 Additionally, hospitals provide diagnosis and treatment details in their claim forms in descriptive form (due to poor awareness of medical coding standards), which further complicates the claims analysis and processing for the insurers.

Health plans introduced in India in the mid-1980s by the government insurers were standardized annual indemnity products covering only inpatient hospitalization. After the industry liberalization in the early 2000s, private insurance firms started to offer health insurance products. Despite the presence of many players, indemnity plans covering inpatient treatment costs continue to be the form that dominates.

Prior to the industry liberalization, health insurance products were provided only by the government insurers, and they depended on unorganized intermediaries to help sell and administer these health products. In order to regulate these intermediaries, the Insurance Regulatory and Development Authority Regulations Act (2001) formalized the role and regulations of these healthcare intermediaries and renamed them Third Party Administrators (TPAs). Although the original role of a TPA was to provide the back-office administrative services to insurance companies, most TPAs today also deal with provider networks and claims payments. Currently there are more than 25 TPAs in operation, processing more than 2.4 million claims annually.

 For these services, the health insurers pay around 5% of the premium collected as fees to the TPAs.

Health insurance products carry a high administrative cost component. Without in-house health expertise, the industry depends on agents and TPAs to market and administer the health business by paying high fees. Hence, adminn istrative expenses are more than 35%, inclusive of agent fees of around 5%, TPA fees of 5%, and insurer’s operational expenses of 20%.

Since the parent insurance firms are focused on other sectors (such as life and property), the firms have no incentive to innovate on health products and prefer to subsidize their health products, resulting in unprofitable operations. With poor product innovation and misaligned pricing and benefit design, the medical expense ratio (also known as the incurred claims ratio) runs at more than 100% of the premium collected for most insurers.

Despite the robust growth in health insurance and increased awareness among consumers, the health insurance industry is not profitable. The combination of high administrative costs and high medical expense ratios has ensured that health insurers continue to have steep losses (

A quick analysis of the above aspects reveals that the industry operates in an environment which is not sustainable in the current form. The external factors (eg, onerous regulations, lack of standards, high claims payouts) and internal factors (eg, high administrative costs, dependence on indemnity models) have forced the health insurance industry into a regressive spiral. In the face of the above challenges, health insurers will need to change their business models to ensure ongoing and sustainable operations.

The expeditious approach for health insurers would be to innovate within the existing operational framework by introducing new products and streamlining the processes to bring

about more efficiency in their operations.

Currently, health products cover only inpatient treatment. Due to the nature of the benefit coverage, the insurer is brought into the care context only at the critical or inpatient treatment phase. The insurer neither has prior insight into the care pathway nor is updated on the earlier treatment stages and protocols. Health insurers have to innovate by offering benefits that cover outpatient, drug, and wellness (preventive care) programs. By expanding the benefits, insurers will get more insight and flexibility into managing the care pathways and can focus more on preventive care and reduce avoidable hospitalization.

Health insurers need to reassess the indemnity-dominated model by introducing plans that incorporate better risk sharing between stakeholders. Given that consumers in India tend to pay a higher out-of-pocket component, firms have to focus on products that combine indemnity with member liability such as copays, coinsurance, and deductibles. Also, given that India is traditionally a savings-led economy, health insurers have to create products with a strong savings and investment component. For example, in the life insurance sector (

), more than 90% of the policies sold have a savings or investment objective (such as money-back, profit-sharing, equity-linked, and mutual fund investment products).

Health insurers also need to widen the target pool of consumers. Currently, almost all health products are targeted at the middle-class segment of the population, with typical annual premiums of around $100 per member for an indemnity with annual maximum cover of $10,000.

 When compared with a mature health insurance market (such as that in the United States), the premium rates in India do not align with the overall health economics of the country (

). For example, the average premium in India is twice the per capita spending on healthcare, whereas in the United States the premium is half the per capita spending on healthcare. This kind of pricing has made health insurance unaffordable for the majority of the population and has resulted in products having limited market acceptance. A more careful assessment of the market and a more realistic pricing of the product, in alignment with the acceptable price points, will ensure wider acceptance of health insurance.

The industry needs to be proactive in controlling administrative costs. Some recent developments seem to suggest that the major players in the industry have recognized this need and have identified key measures for managing administrative costs. One of the recent trends has been to bring TPA services in-house with the objective of saving the TPA fees and providing better customer service.

The above approaches primarily impact the marketing and administration aspects of health insurance and are limited in their effectiveness in addressing the broader challenges in the industry, such as the high medical expense ratios. Hence, health insurers have to evaluate managed care techniques to help bring wider and sustainable improvements to the overall healthcare value chain.

NEED FOR APPLYING MANAGED CARE TECHNIQUES

The current indemnity products from health insurers, although easier to administer, do not promote risk sharing among payers, providers, and members, nor is there any incentive for the providers or members to curtail costs or regulate the use of medical services. By continuing to persist with indemnity models, health insurers are not using the various cost and utilization levers available to them which can ensure a more balanced approach to health services. It is imperative that health insurers in India adapt managed care techniques to effectively manage the health financing and delivery process.

In the current indemnity scenario, members do not carry any liability. Health insurers have to introduce risk-sharing models with increased member liability, including benefits such as coinsurance and deductibles. This model will ensure cost sharing with the member on all treatment expenses and consequently regulate the utilization of acute services and inpatient services.

Indemnity plans encourage patients and hospitals to utilize specialist skills and opt for inpatient treatments. There are no checks and controls to prevent excessive utilization of medical facilities. Introduction of utilization control mechanisms (eg, assigning a gatekeeper or primary care physician, a firstlevel prospective utilization review) will significantly address excessive utilization.

India's emerging health insurance sector faces significant operational challenges that managed care models can help to address.

Health Insurance in India: Need for Managed Care Expertise

 To evaluate the effectiveness of a psychological, family-based intervention to improve diabetes-related outcomes in patients with poorly controlled type 2 diabetes.

 This study was a randomized controlled trial of a psychological family-based intervention targeted at individuals with poorly controlled type 2 diabetes. Recruitment and follow-up occurred at specialist diabetes clinics. Patients were randomly allocated to an intervention group (n = 60) or a control group (n = 61). Poor control was defined as at least 2 of the patient's last 3 glycated hemoglobin (A1C) readings at >8.0%. The intervention consisted of 2 sessions delivered by a health psychologist to the patient and a family member in the patient's home, with a third session involving a 15-minute follow-up telephone call.

 At 6-month follow-up, the intervention group reported significantly lower mean A1C levels than the control group (8.4% [SD = 0.99%] vs 8.8% [SD = 1.36%]; 

 = .04). The intervention was most effective in those with the poorest control at baseline (A1C >9.5%) (intervention 8.7% [SD = 1.16%, n = 15] vs control 9.9% [SD = 1.31%, n = 15]; 

 = .01). The intervention group also reported statistically significant improvements in beliefs about diabetes, psychological well-being, diet, exercise, and family support.

 After participating in a family-based intervention targeting negative and/or inaccurate illness perceptions, patients with poorly controlled type 2 diabetes showed improvements in A1C levels and other outcomes. Our results suggest that adding a psychological, family-based component to usual diabetes care may help improve diabetes management.

	As an adjunct to usual care, a psychological family-based intervention improved glycemic control in patients with poor glycemic control.

		Those with particularly poor control derived the most benefit from the intervention.

		Participants regarded the home setting as a key element of the intervention.

		This intervention could potentially be delivered by any trained member of the multidisciplinary diabetes team.

	Poor glycemic control is a significant challenge in the management of type 2 diabetes, with 36% to 69% of patients

 failing to reach glycemic control targets. These individuals are at an increased risk of developing diabetes-related complications, which impact negatively on their quality of life and have cost implications for healthcare providers.

 There are well-recognized barriers to the successful management of type 2 diabetes, including psychological difficulties and failure to use the expertise of multidisciplinary team members.

	Evidence suggests that people with poorly controlled type 2 diabetes have distinctly different perceptions or beliefs about diabetes compared with people who have good control.

 The current theoretical framework guiding research in this area is the Self-Regulatory Model of Leventhal et al6 (

). According to this model, illness perceptions influence self-management behaviors, which in turn may influence health outcomes. For example, the perception that type 2 diabetes is an acute illness and the belief that diabetes has a negative impact on a person’s life have been found to be associated with poorer metabolic control.

 People with poorly controlled type 2 diabetes are also more likely to report that their diabetes is caused only by genetic factors,

 thereby potentially limiting their motivation to change unhealthy behaviors.

	Thus, interventions focusing on changing negative and/or inaccurate beliefs about diabetes may lead to better self-management and glycemic control. However, family members’ perceptions about diabetes may also influence diabetes outcomes,

 for example, by impacting their decisions to provide support for disease management. Importantly, family-oriented research in type 2 diabetes has been relatively underinvestigated.

 The current study assesses the effectiveness of a psychological family-based intervention for patients with poorly controlled type 2 diabetes.

	This study involved a 6-month prospective randomized controlled trial. Participants were recruited from specialist diabetes clinics at a large suburban hospital. In Ireland, usual care for poorly controlled type 2 diabetes generally involves annual attendance at a specialist outpatient clinic, with interim care provided by family practitioners. There is no national remunerated structured care delivery program; thus, clinicians follow protocols based on international guidelines (eg, those of the American Diabetes Association).

	Patients were included in the study if they had type 2 diabetes for more than 1 year, were over 18 years old, and had persistently poor glycemic control, defined as having at least 2 of their last 3 glycated hemoglobin (A1C) readings at 8.0% or higher. Assessments of A1C coincided with patients’ last 3 clinic visits, generally at the time of recruitment and at 6 and 12 months previously. Patients with a recruitment A1C of less than 8.0% were included if their previous assessments were greater than 8.0%. Patients nominated the family member who was most involved in helping them with their diabetes management to participate. Family members were defined as those having a close relationship and regular contact with the patient, although they were not required to be living with patients or to be a blood relative (eg, a close friend could participate). Family members were required to be  over 18 years old and to have no history of diabetes. Participants provided written informed consent to participate. Ethical approval was granted by the Hospital Ethics Committee.

	Primary outcomes included A1C, illness perceptions (using the Brief Illness Perception Questionnaire),

 and psychological well-being (using the 12-item Well-Being  Questionnaire).

 Secondary outcomes included blood pressure, body mass index, diabetes self-management (using the Summary of Diabetes Self-care Activities Questionnaire),

 self-efficacy (using the UK version of the Diabetes Management Self-Efficacy Scale),

 and family support (using the Diabetes Family Behavior Checklist).

 All questionnaires are psychometrically robust and have demonstrated sensitivity to change (see the trial protocol

Randomization, Allocation Concealment, and Blinding

	An independent statistician (AK) allocated participants to groups by a remote computer-generated random number sequence. Concealment of the allocation sequence was also ensured by randomizing participants after they had been recruited and had completed baseline assessments. Outcome assessors were blinded to group allocation. Due to the psychological nature of the intervention, the psychologist and participants were not blinded.

	The intervention consisted of 3 weekly sessions delivered by a health psychologist (KMK) who had received 16 hours of training in motivational interviewing. The first 2 sessions lasted 45 minutes each and took place in the patient’s home with their family member. The third session involved a 10-to 15-minute follow-up telephone call. Intervention sessions were individually tailored to participants’ needs and attempted to (1) challenge and clarify any inaccurate and/or negative perceptions about diabetes, (2) examine how these  perceptions influenced self-management, and (3) develop written personalized action plans to improve self-management and mobilize family support. The intervention used techniques from health psychology

 such as exchanging information, eliciting change talk, reducing resistance, building self-efficacy, problem solving, and goal setting/action planning. Details are published in the intervention manual.

 Both the intervention and control groups continued to receive their usual diabetes care.

	A power analysis based on A1C and psychological well-being as the primary outcomes indicated that a sample size of 86 gave 80% power to detect an absolute change of 0.9% in  glycemic control and of 3 points on the Well-Being Questionnaire. These changes have been related to clinical outcomes.

Thus, a sample size of 122 (61 per group) was required to ensure at least 80% power, if a response rate of 70% was achieved.

	All analyses were “intention-to-treat” using Stata/SE version 10 (StataCorp LP, College Station, TX). Regression modeling was used to compare primary and secondary outcomes in the comparison groups, with prespecified adjustments made for known prognostic factors, including duration of diabetes and change in response to insulin as fixed effects, and baseline A1C as a covariate. Statistical significance was set at 5% for primary outcomes and at 1% for secondary outcomes. When testing the data for the statistical assumptions underlying regression, it emerged that there was a significant interaction between the independent variable (randomized controlled trial group) and a covariate (baseline A1C). This interaction was not anticipated and was not prespecified in the analysis protocol. However, as recommended by statisticians,

 this interaction effect was controlled for by including it as a covariate and was further investigated by “blocking” participants according to the covariate. A per-protocol analysis excluding participants who did not receive the full intervention was also conducted. These results did not differ from the intention-totreat analysis and are not described.

	A total of 121 patients were recruited (

), 60 of whom were randomized to the intervention group and 61 to the control group. There were no baseline differences between the groups with regard to sociodemographic and clinical characteristics, nominated family members (

), or the proportion of participants showing a trend toward improvements in glycemic control (control group = 14/61 vs intervention group = 18/60). There were also no differences between participants with completed and missing follow-up data or between participants who received the intervention and those who did not (Figure 1). Nonparticipants were still asked to attend for follow-up assessment.

), there was a modest statistically significant difference in A1C of 0.4% between the groups (control group mean of 8.80% [SD = 1.36%] vs intervention  group mean of 8.41% [SD = 0.99%]; P = .04). Interaction effects were investigated by grouping participants into 3 blocks (

): (1) baseline A1C 8.0% to 8.4%, (2) baseline A1C 8.5% to 9.4%, and (3) baseline A1C >9.5%. There were no significant differences in follow-up A1C between groups in block 1 or block 2. In block 3 there was a statistically significant difference of 1.2% in follow-up A1C levels between the groups (intervention mean of 8.70% [SD =1.16%], n = 15, vs control mean of 9.95% [SD = 1.31%], n = 15; B = —1.28, SE (B) = 0.49; P = .01; 95% confidence interval, = –2.29 to -0.26). There were no significant differences between the baseline A1C levels in this block 3 group.

	The intervention group reported statistically significant changes across all illness perception dimensions except “consequences” and “timeline.” Thus, the intervention group reported better personal control, a better understanding of diabetes, and an increased belief in treatment effectiveness. They also reported fewer symptoms and lower levels of diabetes concern and distress. Significantly fewer participants in the intervention group attributed their diabetes to genetic factors, whereas a significantly greater number perceived their diabetes to be caused by a sedentary lifestyle. The intervention group also reported statistically significant improvements in all aspects of psychological well-being, adherence to general dietary and exercise recommendations, diabetes self-efficacy, and family support. There were no differences in fruit, vegetable, and fat intake, blood glucose testing, foot care, body mass index, or blood pressure (

	Process evaluation methods are detailed in the trial protocol. 17 Findings indicate that the intervention was delivered per protocol and was acceptable to participants. Reasons for participants not receiving the intervention (24/60) are detailed in Figure 1.

	Our findings suggest that a psychological family-based intervention for patients with poorly controlled type 2 diabetes led to improvements in glycemic control, diabetes perceptions, psychological well-being, self-management behaviors, and family support.

	However, both groups continue to have unacceptably high A1C levels at follow-up, with neither group achieving optimal glycemic control targets. Nonetheless, the 0.4% decrease in A1C in this study is comparable to improvements associated with other psychosocial interventions for patients with diabetes,

 and a decrease in A1C of 0.5% is increasingly recognized as clinically significant. Although the numbers were small, patients in the most vulnerable subgroup of this population with poor control (baseline A1C >9.5%) showed the greatest improvement in A1C, with a 1.2% decrease. That is comparable to improvements seen after the introduction of new oral hypoglycemic medications.

 Arguably, the intervention was most effective in those with the poorest control because they had the greatest scope for improvement at a psychological and biologic level. As patients in this group are often the most challenging to manage, the approach adopted in this trial may be of value to clinicians and patients alike. A recent pharmacist-managed collaborative care intervention for poorly controlled diabetes also found that some patients benefited more than others.

 When evaluating complex interventions, future research might address the question of what best works for whom, and under what circumstances, so that clinicians and service planners can target such interventions more cost-effectively.

	The intervention group also reported significant improvements in most illness perception dimensions (except consequences and timeline), indicating that patients had a more accurate and positive view of diabetes. Our results are consistent with results of the small number of interventions in other illness populations that have had similar success in changing illness perceptions.

 Baseline scores on the timeline dimension indicated that participants had an accurate view of diabetes as a chronic condition; thus, there was little scope for improvement. With regard to consequences, there was a significant improvement in the intervention group when nonparticipants were excluded from the analysis.

	There was little scope for changes in blood pressure, as both groups had acceptable control at baseline. The lack of any change in body mass index is in keeping with other family intervention studies

 and highlights the difficult task faced by patients in controlling their weight. The lack of improvements in specific self-management behaviors may have arisen as participants were given the opportunity to select the behaviors targeted for change. This raises questions about interventions that emphasize patients’ own goals for  change, as specific self-management behaviors (eg, foot care) may be undervalued and not addressed.

	The key strengths of this study include its firm theoretical base, its mixed-methods approach, its multidisciplinary team input, and its adherence to the high-quality practices recommended for randomized controlled trials.

 These factors, as well as the pattern of improvements across primary and secondary outcomes, suggest that internal validity was  high. However, given the high “did not attend” rates at recruitment clinics, and the consistent association between infrequent clinic attendance and adverse clinical outcomes,

 it is  likely that the most vulnerable of those with poor glycemic control (ie, those who default from care) were not recruited. However, it was not possible to analyze this group, as we had no consent to access their records. Additionally, in terms of controlling for medication change, only a change from oral hypoglycemic agents to insulin was included in the analysis. It was not possible to collect data on other types of medication changes, although this is an important factor that future studies may want to consider. Also, the current study would ideally have included a longer follow-up (eg, 12 months) to investigate whether observed improvements were maintained.

	We were unable to identify from the literature any other home-based interventions for patients with poorly controlled type 2 diabetes. However, home-based interventions for other groups

 have been found to be acceptable and convenient to patients, and may help to educate family members and encourage family involvement in disease management. Such interventions may also be more effective at accessing vulnerable populations, including the elderly, ethnic minorities, patients from socially deprived areas, patients with multimorbidity, and those who fail to attend regular clinic-based care. The increased costs associated with home-based interventions, however, ought to be balanced against their overall effectiveness. Future studies may consider implementing this kind of intervention in a clinic setting, alongside a full cost-effectiveness analysis, to determine whether it has similar positive effects. While this intervention was delivered by a psychologist, other health professionals could be trained to deliver it in other contexts. This is an important consideration in terms of the effective future implementation of such psychological interventions into routine care.

	Without a patient-only intervention arm, it was not possible to conclusively elucidate the effects of including a family member. However, the robust theoretical framework underlying the intervention, coupled with appropriate process and outcome measures, suggests that the family member was a key active ingredient. The majority of the sample (75%) nominated their spouses, and the remaining proportion proposed their children. The study was not powered to detect whether there were any differences in outcomes depending on the family relationship, and this factor may be an area for future research. Eight patients did not receive the intervention because their family member declined to participate. It is possible that mobilizing family support is most important in this group. However, it also may be possible to deliver the intervention to these patients individually. Although additional arms increase trial complexities and costs, it may be worthwhile to conduct future studies comparing a family intervention with a patient-only intervention.

	Finally, it must be noted that the delivery of this intervention was challenging and time consuming, due in part to the difficulties in accessing participants despite repeated efforts. Clinicians and service planners will recognize the existence of a hard-to-reach group of high-risk patients who frequently require unplanned care with repeated hospital admissions  to deal with the complications of poor control. Continued persistence and flexibility may be the solution to accessing these vulnerable populations in both research and clinical  settings, although these interventions initially may be more costly and time consuming. In the long term, such interventions may improve illness outcomes for patients, thereby reducing costs and improving quality of life for patients and families.

	This study indicates that targeting inaccurate and/or negative beliefs about poorly controlled type 2 diabetes, in the home setting and in the presence of a family member, can change illness perceptions and improve poor glycemic con trol, self-management, psychological well-being, and familysupport. Given the resource-intensive nature of this  intervention and the modest improvement in glycemic control, future studies are needed to assess the effectiveness of the delivery of this type of intervention delivered in alternative settings, and by other health professionals. Tailoring these kinds of interventions to those with the poorest of control may deliver the most benefit, at least in the short term.

A family-based intervention targeting negative and/or inaccurate illness perceptions in patients with poorly controlled type 2 diabetes was effective in improving glycemic control.

February 2011

Outpatient Wait Time and Diabetes Care Quality Improvement

Excess Hospitalization Days in an Academic Medical Center: Perceptions of Hospitalists and Discharge Planners

Health Insurance in India: Need for Managed Care Expertise

Psychological Family Intervention for Poorly Controlled Type 2 Diabetes

Effect of Multiple Chronic Conditions Among Working-Age Adults

Abolishing Coinsurance for Oral Antihyperglycemic Agents: Effects on Social Insurance Budgets

Behavioral Health Disorders and Adherence to Measures of Diabetes Care Quality

Effects of Nonadherence With Prescription Drugs Among Older Adults

Timing of Follow-up After Abnormal Screening and Diagnostic Mammograms