The Health Care Manager Volume 33, Number 1, pp. 30–37 Copyright # 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins

Organizational Factors Influencing Health Information Technology Adoption in Long-Term-Care Facilities Tiankai Wang, PhD; Yangmei Wang, MBA, MST, CPA; Jackie Moczygemba, MBA, RHIA Long-term care (LTC) is an important sector of the health care industry. However, the adoption of health information technology (HIT) systems in LTC facilities lags behind that in other sectors of health care. Previous literature has focused on the financial and technical barriers. This study examined the organizational factors associated with HIT adoption in LTC facilities. A survey of 500 LTC facilities in Texas enabled researchers to compile HIT indexes for further statistical analyses. A general linear model was used to study the associations between the clinical/administrative HIT indexes and organizational factors. The empirical outcomes show that the size of an LTC facility has a significant association with HIT adoption. Rural LTC facilities, especially freestanding ones, adopt less HIT than their urban counterparts, whereas freestanding LTC facilities have the lowest HIT adoption overall. There is not enough evidence to support ownership status as a significant factor in HIT adoption. Some implications are proposed, but further research is necessary. Key words: health information technology, HIT index, long-term care, organizational factors

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HE AGING OF the US population and the projected growth of the oldest age bracket (85 years or older) will have a major effect on the demand for and supply of long-term-care (LTC) services along with the resources needed to provide those services.1 Long-term-care providers care for this fastest-growing segment of the population and account for a high proportion of health care dollars spent.2 Health information technology (HIT), such as electronic medical records, computerized

Author Affiliations: Health Information Management Department, School of Health Professionals, Texas State University, San Marcos (Dr T. Wang and Ms Moczygemba), and Accounting Department, Dillard College of Business Administration, Midwestern State University, Wichita Falls (Ms Y. Wang), Texas. The authors report no conflicts of interest. Correspondence: Tiankai Wang, PhD, Health Information Management Department, School of Health Professionals, Texas State University, San Marcos, TX 78666 ([email protected]). DOI: 10.1097/01.HCM.0000440624.85255.8e

physician order entry, and decision support systems, is used to collect, store, retrieve, and transfer clinical, administrative, and financial health information electronically.3 Health information technology has the potential to reduce errors, improve quality of care,4,5 and deliver health care more efficiently.6 Long-term-care providers can ‘‘achieve an increase of 37% in administrative productivity’’ by using HIT over time,7 but empirically, few studies have assessed the impacts of HIT in LTC facilities.8 Although HIT applications that positively affect both quality of care and patient safety currently exist, they are not widely used in the current LTC setting.9 Long-term-care facilities lag in HIT adoption, compared with the other sectors in health care,10-12 and existing technology may be underutilized.13 Previous studies have mainly focused on identifying the financial and technical barriers10,14-17 in adopting HIT. Studies are lacking on the identification of organizational factors such as bed size, location, and ownership, which may have an impact on HIT adoption. In other words, what are the organizational factors that enable

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Factors Influencing HIT Adoption or hinder HIT adoption in LTC facilities? The purpose of this study was to identify the organizational factors associated with LTC facilities in adopting HIT systems. LITERATURE REVIEW Earlier, somewhat dated, studies18-20 agree that organizational factors are associated with organizations’ adoption of technological innovations. However, there is less agreement on which specific organizational factors are involved. Based on the literature, 4 organizational factors are identified as being salient to HIT adoption in the health care industry: (1) size (number of beds), (2) location (urban versus rural), (3) system affiliation (hospital-based versus freestanding vs systems affiliated), and (4) ownership status (profit vs nonprofit). Organizational behavior theory has consistently identified size as the most important organizational factor predicting innovation adoption among organizations.18,21 Economies of scale suggests that large organizations can use expensive equipment more efficiently and tend to have more financial resources that they can devote to implementing new technologies. Lacking such resources, smaller organizations are forced to make difficult tradeoffs in their investment choices and often forgo implementation of expensive technologies. Empirically, scholars had different findings. Wang et al22 and Li et al23 found that hospital bed size was significantly and positively related to the adoption of all categories of health care information systems. However, in a Florida study,24 hospital size was not significant in HIT adoption. Thus, it is postulated as follows: Hypothesis 1: Holding all other relevant factors constant, LTC size will be positively associated with HIT adoption. Location tends to have a significant association with an organization’s HIT adoption. Longterm-care facilities located in urban areas have greater access to financial, infrastructural, and human resources. This occurs because an urban facility is near funding agencies, support organizations such as Internet service providers, and can more easily recruit and maintain skilled technical staff. Burke et al20 state that urban

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hospitals are associated with higher adoption of HIT compared with those in rural locations. Hence, it is postulated as follows: Hypothesis 2: Holding all other relevant factors constant, urban LTC facilities will have a higher adoption of HIT than rural ones. Hospitals are leading HIT adoption in the health care industry.10 Hospital-based LTC facilities tend to have better resources and technical support in adopting HIT. In addition, the diffusion of innovation theory identifies that communication channels play a pivotal role in the adoption of technology.25 Carey26 suggested that certain administrative processes such as insurance billing and physician integration tend to benefit from system membership because these processes can be standardized and supported from a central location. Menachemi et al9 and Li et al23 also state that system-affiliated hospitals appear to have a higher adoption rate in HIT applications than freestanding ones. Hence, it is postulated as follows: Hypothesis 3: Holding all other relevant factors constant, hospital-based and systemaffiliated LTC facilities will have a higher adoption of HIT than freestanding ones. Long-term care’s ownership status can be categorized into for-profit and nonprofit. Forprofit facilities are expected to have a greater propensity to adopt HIT than nonprofit or government-owned because the former tend to be more sensitive to operational costs and efficiencies and view HIT as a means of improving both. Some studies state that ownership status may have a positive correlation on HIT investment policies.20,27,28 Therefore, it is postulated as follows: Hypothesis 4: Holding all other relevant factors constant, for-profit LTC facilities will have a higher adoption of HIT than nonprofit ones. METHODS Different from the HIT utilization data available to hospitals, such as the Healthcare In formation and Management Systems Society Analysis Database (the previous Dorenfest Database), there is no existing database regarding LTC facilities’ HIT adoption and utilization. The

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THE HEALTH CARE MANAGER/JANUARY–MARCH 2014

last nationwide survey on nursing home HIT adoption was conducted in 2004.17 Because of the fast development in HIT, the 2004 data are now obviously obsolete. In 2010, the US Department of Health and Human Services developed 2 survey instruments on ‘‘HIT adoption and use in nursing home.’’ Unfortunately, no further report was published. And consequently, no national data are available. Therefore, the authors gathered the primary data in this research to construct the survey instrument. Based on the literature review, a questionnaire was developed. In addition to the questions in the US Department of Health and Human Services 2010 survey, which includes the minimum data set, the clinical functions, and the barriers to HIT adoption and use, the research questionnaire also collected information about LTC facility characteristics, HIT implementation status, the administrative functions, and LTC administrators’ perspectives on HIT adoption and utilization. A pilot survey instrument was mailed in the beginning of 2011 to nearby LTC facilities to assess the validity and reliability of the questionnaire. Based on the responses from the pilot survey, a few survey questions were edited.

With a limited budget, the authors could survey only 500 LTC facilities from a total of 1177 facilities in Texas whose addresses were generated from the database in the official Medicare Web site (www.medicare.gov/NHCompare). To draw a random sample, the facility information for all 1177 LTC facilities was input into an SPSS data set. Five hundred facilities were randomly selected by using the ‘‘Select Cases’’ function under ‘‘Data’’ tab in SPSS. In fall 2011, a final survey instrument was mailed to the 500 randomly selected LTC facilities in Texas, addressed to the facility’s administrator. Fifty-five survey questionnaires were returned because of voided mailing addresses. Therefore, the valid population size in this survey is 445. Thirty days after the first round of survey questionnaires was mailed, follow-up surveys were mailed to those LTC facilities with nonresponse. Ultimately, the survey was completed by 134 LTC facilities for a response rate of 30%. The demographic characteristics of survey respondents are listed in Table 1. The authors compared the LTC facility characteristics proportions between the survey sample and the entire population in Texas in Table 1. It was found that except for the for-profit proportion, all other

Table 1. Demographic Characteristics of Survey Respondents Survey Respondents Characteristics Ownership status For- profit Not-for-profit Location Rural Urban Affiliation type Hospital based Freestanding System affiliated No. of beds 0-49 50-99 100-149 150

All Texas LTC Facilitiesa

Comparison

%

n

%

n

72.39 27.61

97 37

85.30 14.70

1004 173

3.86 3.86

0b 0.99

53.73 46.27

72 62

59.80 40.20

704 473

1.36 1.36

0.09 0.91

6.72 32.84 60.45

9 44 81

n/a n/a n/a

n/a n/a n/a

5.97 35.82 41.04 17.16

8 48 55 23

6.80 31.90 47.70 13.60

80 375 562 160

0.36 0.93 1.47 1.13

0.36 0.82 0.07 0.87

Z

P

Abbreviation: LTC, long-term care. a Information about ownership status and number of beds of all Texas LTC facilities is retrieved from medicare.gov. Information about location was retrieved from the Rural Assistance Center Web site at http://ims2.missouri.edu/rac/amirural/, where only ‘‘rural’’ and ‘‘urban’’ information is available. In the proportion comparison, ‘‘urban’’ in the sample includes suburban, urban, and metropolitan statistical area. b P < .001.

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Factors Influencing HIT Adoption proportions of respondent characteristics are not significantly different from those of the whole state of Texas. Therefore, the survey sample is representative of the population of the LTC facilities in Texas. MODEL AND MEASURES Per the previous literature review, the HIT adoption index might be associated with the organizational factors including size, location, affiliation types, and ownership. As thus, the basic regression model is as follows: HIT index = 0 + 1 size + 2 location + 3 affiliation + 4 ownership +  Because clinical and administrative areas are 2 clearly substantively different areas, the HIT index was further categorized into the administrative HIT index and the clinical HIT index. Therefore, the regression models are as follows:

(1). administrative HIT index = 0 + 1 size + 2 location + 3 affiliation + 4 ownership +  (2). clinical HIT index = 0 + 1 size + 2 location + 3 affiliation + 4 ownership +  The dependent variables, extent of HIT adoption, were measured as a raw count of HIT applications checked by respondents and also individual counts of applications checked within each application cluster. The administrative HIT index ranged from 0 (if an LTC had no administrative HIT on the list) to 14 (if that LTC has implemented all 14 administrative HIT functions on the list). Likewise, the clinical HIT index ranged from 0 to15. The administrative and clinical functions in the survey questionnaire and the response rate for each function are reported in Tables 2 and 3. The definitions of the functions are reported in the footnotes in Tables 2 and 3. The descriptive statistics of the 2 dependent variables are reported in

Table 2. Administrative Functionalities Survey Respondents Administrative Functions Finance Billing Claim submission Accounting Payroll Others Admission Discharge Transfer Human resources information management Summary of services provided Eligibility information Scheduling Inventory management Prior authorization Appointment reminder

%

n

77.61 75.37 64.93 62.69

104 101 87 84

58.21 50.75 44.03 37.31 30.60 23.13 20.15 17.91 17.16 13.43

78 68 59 50 41 31 27 24 23 18

Notes: Billing refers to the process of claims preparation, including coding. Claim submission refers to the process that claims are submitted to clearinghouses or insurers. Accounting refers to the other accounting functionalities besides billing, claim submission, and payroll. Payroll refers to the process of distribution of the paychecks to employees each payday. Admission refers to the applications applied in the resident check-in processing. Discharge refers to the applications applied in the resident check-out processing. Transfer refers to the applications applied in the resident transferring processing. Human resources information management refers to the processes at the intersection between human resources management and information technology, such as online recruitment, hiring, training, and so on. Summary of services provided refers to the applications applied in summarizing services provided. Eligibility information refers online system that provides timely and accurate information regarding a recipient’s eligibility for services. Inventory management refers to the processes involved in the stocked goods management, such as ordering, shipping, handling, stocking, and so on. Scheduling refers to making arrangements for clients and internal staffs. Prior authorization refers to the functionality which can submit preauthorization requests to the insurers. Appointment reminder refers to the applications which automatically check physicians’ schedules and notify patients of upcoming appointments.

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Table 3. Clinical Functionalities Survey Respondents Clinical Functions Resident assessment Minimum data set Resident assessment protocols Triggers Computerized physician order entry Physician orders Laboratory/procedures information Medication orders and drug dispensing E-prescribing between practitioner and pharmacy Care management Individual care management plan Census management Dietary Treatment administration information Clinical charting applications, including assessment and progress notes Clinical decision support tools Electronic access to an assigned care manager Receiving external clinical documents

%

n

85.07 72.39 61.19

114 97 82

56.72 33.58 32.09 8.96

76 45 43 12

58.96 50.00 34.33 28.36 23.13 19.40 14.93 13.43

79 67 46 38 31 26 20 18

Notes: Minimum data set refers to the utilization of minimum data set. Resident assessment protocols refers to the utilization of resident assessment protocols. Triggers refer to the utilization of triggers. Physician orders refer to the processes of electronic entry of medical practitioner instructions for the treatment of residents under his or her care. Laboratory/procedures information refers to a class of applications that receives, processes, and stores information generated by medical laboratory processes. Medication orders and drug dispensing refers to the processes during the preparation, packaging, labeling, record keeping, and transfer of a prescription drug to a resident. E-prescribing refers to the electronic transmission of prescription information from the prescriber’s computer to a pharmacy computer. Individual care management plan refers to a collaborative process of assessment, planning, facilitation, and advocacy for options and services to meet a resident’s needs. Census management refers to patient demographics. Dietary refers to the information technology application to manage residents dietary. Treatment administration information refers to the ability to manage the residents’ treatment information. Clinical charting applications refer to the applications in clinical charting, including assessment and progress notes. Clinical decision support tools provide best practice suggestions for care plans and interventions based on clinical problems or diagnoses. Electronic access to an assigned care manger allows an existing or perspective resident to access an assigned care manager online. Receiving external clinical documents refers to the ability to receive external clinical documents about the residents, including provider notes, laboratory data, radiology data, medical devices, patient history, and so on.

the outstanding interactions are reported. The general linear model results are presented in Table 5. The authors checked the residuals of dependent variables after model fitting. The residuals indicated that the model fit was adequate and that the data did not violate the multiple regression assumptions. (Note: First, Kolmogorov-Smirnov and Levene tests are not significant. So residuals are independent samples from normal distributions and the models have constant variation. Next, the residual points

Table 4. Based on the hypotheses proposed above, the general linear model was used to study the associations between administrative/ clinical HIT index and the organizational factors. In each case, size, geographic location, system affiliation, and ownership status are the independent variables. The analysis was run with SPSS version 18. During the analysis, the full factorial model was run; that is, all interactions between the independent variables were tested. Most interactions are not significant, so only

Table 4. Descriptive Statistics of Dependent Variables

Administrative HIT index Clinical HIT index

Frequency

Mean

SD

Minimum

Maximum

134 134

7 6

4 4

0 0

14 15

Abbreviation: HIT, health information technology.

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Factors Influencing HIT Adoption Table 5. General Linear Model Analysis Results Dependent Variables Administrative HIT Index Size

Location Affiliation

Ownership Interactionf Adjusted R2

0-49 50-99 100-149 150 Rural Urban Hospital based Freestanding System affiliated For-profit Non-for-profit Rural  hospital-based Rural  system-affiliated

5.238 0.715 0.465 0d 2.008 0d 5.609 0.1 0d 1.285 0d 3.377 2.156 0.217

( 4.046) ( 2.434) ( 1.959)

Clinical HIT Index a b c

( 1.100) (3.039) (0.132) (1.576) (0.554) ( 0.385)

e

4.207 2.19 1.252 0d 9.857 0d 8.845 7.293 0d 0.69 0d 13.954 12.133 0.235

( 2.458) ( 2.544) ( 1.932)

b

( 2.848)

e

(2.386) ( 2.544)

b

b c

b

(0.849) (2.299) ( 2.996)

b e

The numbers in the parenthesis are the t values. Abbreviation: HIT, health information technology. a P < .001. b P < .05. c P < .1. d This parameter is set to zero as a base for comparison by SPSS. e P < .01. f Full factorial model was tested; that is, all interactions between the independent variables were tested. But only the outstanding interactions are reported in this table. Most ‘‘size  rural’’ interaction parameter estimates are statistically significant, but not reported in this table.

fluctuate randomly around zero in an unpatterned fashion. Therefore, the model in the research meets the multiple regression assumptions: normality, homoscedasticity, and linearity). DISCUSSION This study empirically demonstrated that among Texas LTC facilities, some organizational factors, including size, affiliation status, and locations, have significant associations with the adoption of various HIT applications and HIT applications in general. The findings suggest that not all organizational factors are good determinants of HIT adoption in Texas LTC facilities. Whereas some factors may have stronger associations on the dependent variables than others, other factors may have no significant association. First, LTC facility size is significantly associated with clinical and administrative HIT adoption indexes. In the general linear models, the parameter estimates of size are significantly negative and decrease when there is an increase in facility size. This means that the smaller the LTC facility’s size, the less HIT index it has. The result matches the economics of scale

notion and some previous studies in the hospital setting.22,23 The huge financial requirements in HIT adoption mean that the large LTC facilities have more advantages than the smaller ones. Second, being in a rural location is negatively associated with the HIT indexes but is significantly associated only with the clinical HIT index. This means rural LTC facilities have a lower clinical HIT adoption rate than their urban counterparts, but comparable adoption scales in administrative HIT adoption. Normally, clinical HIT adoption lags behind administrative HIT adoption.20 This outcome shows that the issue of low clinical HIT adoption is significant in rural LTC facilities. Further interaction effect analysis reveals more details about the situation. We can see that ‘‘rural  hospital-based’’ has a significant positive parameter estimate in the clinical HIT index model and negative effect attributes from the freestanding facilities. Therefore, rural freestanding LTC facilities suffer most in clinical HIT adoption. Third, affiliation status has a complex outcome. The parameter estimates of hospital-based LTC facilities are significantly positive in both

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THE HEALTH CARE MANAGER/JANUARY–MARCH 2014

models. Therefore, the results indicate that hospital-based LTC facilities are leading HIT adoption more than their counterparts. The parameter estimate of freestanding is significantly negatively associated only with the clinical HIT index but is not significant in the administrative HIT model. Thus, the results indicate that freestanding LTC facilities adopt less clinical HIT when compared with hospitalbased and system-affiliated facilities, but in administrative HIT adoption, the freestanding LTC facilities are not significantly different from system-affiliated ones. Fourth, profit status has a positive association in both HIT indexes, but neither of them is statistically significant. The positive associations support the theory that for-profit facilities are expected to have a greater propensity to adopt HIT. The insignificant outcomes are contrary to the initial expectations and some empirical evidence from the hospital setting. Two explanations may apply to this result. The first one is that the result is valid, and it shows that LTC is a special sector and different from the other sectors in health care. Tax status does not have a significant effect on HIT adoption. The other explanation is that the result is biased, because ‘‘for-profit’’ status is the only unrepresentative organizational factor in the sample (Table 1). The insignificant effect of tax status could be because of the biased sample from the survey. If a more representative sample is gathered, the result may be different given that further research is necessary. The study has a number of limitations that point to directions for future research. First, as seen from Table 4, the adjusted R2 in the 2 models are not high, ranging from 0.217 to 0.235. The low adjusted R2 suggests that only about 20% of the HIT adoption indexes in the sample of Texas LTC facilities were explained by the 4 organizational factors and their interactions in the models. Besides the 4 organizational factors, more determinants are associated with an LTC facility’s decision on HIT adoption. Second, there is no unanimous agreement on measuring HIT adoption. This research borrows the design of Burke et al.20 This design has 2 assumptions: each HIT element is equal to the others, and different HIT elements are perfect

substitutes. Those may not be true. When better measurement is available, the authors would like to retest the data. Third, 1 argument suggests that an effective organizational structure is determined by its technology29; that is, on the contrary, technology and organizational factors have a mutual impact. In health care, Harrison et al30 state that HIT changes existing organizations. How multiple information systems are integrated in LTC facilities’ management and the impact on their performance should be addressed in further research. Finally, the sample for this study was only from Texas, which limits the generalization of results. Further research is necessary to explore the organizational factors’ association with LTC facilities’ HIT adoption. CONCLUSION Despite the foregoing limitations, this study represents the first attempt to assess directly and empirically the associations between the organizational factors and LTC HIT adoption. The results therefore have significant implications even as we await more representative and controlled studies in the future. Limited by available resources, small LTC facilities have lower HIT adoption scales than large ones. As for location, rural LTC facilities lag behind most in clinical HIT adoption, especially rural freestanding facilities. Currently, LTC facilities do not benefit from the Health Information Technology for Economic and Clinical Health Act incentive for HIT adoption. When the government extends further incentive programs to LTC, policymakers should give special considerations to small and rural freestanding LTC facilities. Besides waiting for help from the legislators, LTC administrators can also do something based on the findings of this study. Organizational factors, by their nature, are not easily changed, but some changes are possible. For example, more hospitals are acquiring nursing homes or forming stronger affiliation agreements in light of financial pressures to reduce 30-day readmissions.31 Moreover, freestanding LTC facilities have the lowest HIT adoption rates. Therefore, if a freestanding LTC facility joins a system-affiliated group, the LTC facility can

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Factors Influencing HIT Adoption increase its HIT adoption and, in the long run, improve its productivity. To encourage such

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changes, policymakers and relevant stakeholders should provide the necessary support.

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Organizational factors influencing health information technology adoption in long-term-care facilities.

Long-term care (LTC) is an important sector of the health care industry. However, the adoption of health information technology (HIT) systems in LTC f...
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