The use of health information technology in publicly-funded U. S. substance abuse treatment agencies.
Health information technology
Health IT encompasses a broad range of hardware/software, systems, and applications that support the delivery of patient or population care, or support patient self-management. Health IT applications include electronic medical records, telemedicine, clinical alerts and reminders, computerized provider-order entry, clinical decision support systems, electronic results reporting, electronic prescribing, consumer health informatics/patient decision support, mobile computing, data exchange networks, knowledge retrieval systems, administrative and financial systems, and other electronic exchanges of health information (Chaudhry et al., 2006). Health information includes clinical data on clients' medical and behavioral status, as well as data related to financial, regulatory, and other required reporting. Most commonly, these applications are implemented as enterprise-wide networks, and staff access them via desktop or laptop computers, but some applications can be accessed via, or reside on, cellular phones, personal digital assistants, or touch-screen kiosks.
The Institute of Medicine (2003) identified eight key functional capabilities of a health IT system: (1) clinical documentation (health information/data); (2) results management; (3) order entry management; (4) decision support; (5) electronic communication and connectivity; (6) patient support; (7) administrative processes; and (8) reporting and population health. These key capabilities clearly benefit hospital environments that must manage information from multiple departments (e.g., radiology, pharmacy, intensive care) and with multiple kinds of quantitative data (e.g., prescription orders, lab results). In contrast, most substance abuse treatment programs have primarily psychosocial interventions (99% provide individual and 96% provide group therapy) and are less likely to offer medical interventions (29% offer HIV testing and 21% test for other sexually transmitted infections) (SAMHSA, 2008).
In addition to the administrative management of information, health IT provides organizational opportunities. It can help track client data over time, providing a longitudinal picture of health to aid in managing chronic diseases. Health IT offers providers patient-tailored alerts on issues, such as vaccine schedules, to increase the quality of preventive care. Data can be aggregated across patients to measure quality indicators. Staff may increase productivity without delay for inputting from hand-completed forms or duplicative data entry across providers or visits. Billing schedules may be optimized by allowing electronic invoicing, thus reducing delays in communication when paper invoices are mailed or faxed. Health IT can also reduce medical mistakes due to lack of communication, missing information, or illegible handwriting (Frieden & Mostashari, 2008; Nembhard, Alexander, Hoff & Ramanujam, 2009; Sidorov, 2006). These benefits are most useful from health IT applications that track many patients, creating economies of scale in large hospitals that may not be applicable to treatment programs, which tend to have fewer patients.
The effectiveness of a program's health IT depends on the established goal: boosting quality of care, identifying areas for improvement, reducing medication errors, optimizing staff time, or increasing cost effectiveness. Decision support systems have been associated with increased quality through more consistent adherence to protocol-based care for preventive care measures, such as higher vaccination rates and fewer post-operative infections (Chaudry et al., 2006; Garg et al., 2005). Non-automated clinical monitoring based on large-scale screening and data aggregation has helped organizations identify areas for improvement. For example, Evans and colleagues (1993) used electronic medical records to identify adverse drug events and develop interventions to reduce their frequency. Koppel and colleagues (2005), however, found that implementing a computer order-entry system increased the likelihood of medication error risks due to data fragmentation and flawed human-machine interface. Veterans Affairs researchers found limitations in the accuracy of the automated quality measurement, such as high false-positive results and underestimates of completion of quality-of-care processes (Kramer et al., 2003; Kerr et al., 2002). We could find no studies that addressed the accuracy of nonautomated data, and few studies demonstrated the specific use of health IT to gather information broadly for specific quality-improvement efforts. Finally, results are also mixed on evaluations of staff-time reduction from implementing health IT for clinical documentation; one study found that physicians spent more time on clinical documentation, but nurses spent less time (Poissant, Pereira, Tamblyn, & Kawasumi, 2005).
Few evaluations of the cost of health IT implementation and cost-effectiveness in substance abuse treatment are available; studies that document these issues in physician practices and hospitals were typically limited to changes in utilization of services due to health IT, and excluded cost data on system implementation or maintenance (Chaudry et al., 2006; Goldzweig, Towfigh, Maglione & Shekelle, 2009; Heathfield, Pitty & Hanka, 1998). Efforts are also mixed in reducing patient service utilization by increasing efficiency from health IT (Chaudry et al., 2006; Sidirov, 2006). Increased cost savings can arise from increased efficiency in billing practices rather than increased efficiency in clinical practice (Sidirov, 2006). No large-scale studies have been identified that directly relate health IT implementation to costs and quality of care.
Finally, much has been written about barriers and facilitators to implementing health information technology (Frieden & Mostashari, 2008; Jha et al., 2009; Jeyaraj, Rottman, & Lacity, 2006; Wisdom, Gabriel, Edmundson, Bielavitz, & Hromco, 2008). Briefly, the health IT implementation process includes multiple considerations. Concrete and structural factors include the specific technological elements (e.g., choice of system), resulting changes in organizational processes and workflow, and project management for the implementation process. In addition, others suggest successful implementation also includes attention to the sociotechnical environment, such as norms, culture, policies, and values of the organization (Amarasingham, Plantinga, Diener-West, Gaskin, & Powe, 2009), and to human factors such as staff acceptability, training, and the system's user-friendliness (Chaudry et al., 2006). Barriers to successful health IT implementation include: cost, workforce aversion, lack of interest, conflicting leader and workforce goals, lack of incentives, provider resistance, limited participation, partial compliance or underreporting, and gaming of programs (Frieden & Mostashari, 2008; Jha et al., 2009; Jeyaraj, Rottman, & Lacity, 2006; Nembhard et al., 1999; Wisdom et al., 2008). Implementation barriers and facilitators are discussed in more depth below.
Electronic medical records
Effectively implemented electronic medical records are a necessary, but not sufficient, component to improve the performance of health care systems (Hillestad et al., 2005). Well-designed electronic medical records ensure that providers have appropriate client information at the right time and in the right format to make decisions about client care before, during, and after clinical encounters (Frieden & Mostashari, 2008). Electronic medical records require standardized data elements for consistent reporting and comparison of key indicators. Health records can be programmed to generate reminders for preventive care (Frieden & Mostashari, 2008), recommended dosage or interaction alerts for medications (i.e., through computer-provided order entry; Kaushal & Bates, 2001), and allow aggregation of data for providers to answer questions about clients' demographics and insurance coverage, the number of patients with specific disorders, and the types of treatment clients received.
A fully integrated electronic medical record system includes business and clinical applications. The business applications include patient administrative systems for admission, discharge, and transfer, registration, scheduling, and financial management systems that may include billing, materials management, staff scheduling/time/attendance, and payroll. Currently, many substance abuse programs have partial implementation of these systems (Wisdom et al., 2006). For example, many programs have electronic billing processes but much less commonly have full implementation with all of the elements mentioned above. Electronic medical records can greatly simplify administrative tasks and facilitate increased and timely reimbursement.
Electronic medical records for clinical applications include electronic lab or pharmacy data, health records for demographic information, computerized client case notes or group notes, and automated case management (e.g., for mental health or employment services). The prevalence of electronic medical records for clinical applications varies widely; partial implementation is standard, with programs having at least some demographic information maintained in an electronic format. Few programs have complete information including electronic case notes and pharmacy orders (e.g., for buprenorphine). Electronic medical records can streamline and link services for all clinical staff to quickly view all records for a client, especially valuable in crises. Drawbacks of electronic medical records for clinical applications include the substantial costs for purchase and implementation. Partial implementation or unlinked systems can lead to inefficiencies, such as double-entry, and increased likelihood of error. Further, fully integrated electronic medical records also raise privacy concerns about computer access limitations, particularly the potential for errors in inputting or interpreting data (Ash, Berg & Colera, 2004).
Programs' development of electronic medical records for clinical applications has the potential to improve programs' ability to view aggregate client data and conduct systematic outcomes evaluation, but this is not necessarily the case (Frieden & Mostashari, 2008). For example, of 23 programs in the first cohort of sites participating in the Network for the Improvement of Addiction Treatment in 2003, only 4 (17%) reported they were able to produce client-level process data (e.g., percentage of admitted clients who attended at least two more sessions) entirely from existing electronic data systems (Wisdom et al., 2006). Most of these programs (n = 19, 83%) reported that they were using multiple, non-linked methods and data sets to determine their clients' show rates and retention in services. Ducharme, Knudsen, and Roman (2005) evaluated 763 substance abuse treatment programs in the National Treatment Center Study in 2002-2003 and found that, although almost 70% of drug treatment programs report having intake data (basic demographics collected upon client program entry) computerized, 42% report having more detailed clinical assessment information computerized, and less than one-third have electronic medical records. Program size, as determined by the number of admissions per year, predicted a greater likelihood of electronic medical records. While these findings indicate low rates of such records among substance abuse treatment programs, Jha and colleagues (2007) found that through 2005, only 24% of physicians used electronic medical records; the authors indicated lack of information was a substantial barrier.
The cost of implementing electronic medical record systems is a primary limitation for substance abuse treatment programs records. Data specific to substance abuse treatment programs on implementation cost and cost-effectiveness are not available, but a few studies of solo or small-group primary care practices may reflect the situation substance abuse treatment programs face. Miller, West, Brown, Sim, and Ganchoff (2005) conducted case studies of 14 solo or small-group primary care practices about the implementation and cost-effectiveness of electronic medical record software. They report that initial costs of implementing electronic medical records averaged $44,000 per full-time-equivalent physician, and ongoing costs averaged $8,500 per provider per year. The average practice recouped costs in 2.5 years and continued to experience financial benefits; some practices experienced more financial risks, and most practices devoted considerable time to implement and adjust to the new systems. Wang and colleagues' (2003) study of electronic medical record implementation found an estimated net benefit from using a system for a 5-year period of $86,400 per primary care provider, with benefits primarily from savings in drug expenditures, improved utilization of radiology tests, better capture of charges, and fewer billing errors. These results may differ with those of substance abuse providers, because they are more likely to be publicly-funded and have much lower reimbursement rates than primary care physicians.
Federal and state confidentiality regulations have also limited the expansion of electronic medical records to addiction treatment (McCarty, Kunkel & Campbell, 2009; Shay, 2005). Section 42, Code of Federal Regulation, Part 2 (42 CFR Part 2), the Confidentiality of Alcohol and Drug Abuse Patient Records Regulations, implemented federal standards to protect the confidentiality of clinical records for individuals treated for alcohol and drug disorders. The regulations were enacted to protect the privacy of individuals entering alcohol or drug treatment, but their application to the sharing of data vis-a-vis electronic medical records has not been confirmed. The privacy and security standards of the Health Insurance Portability and Accountability Act (HIPAA) of 1996 require patient consent for release of treatment information and may present a barrier to inter-program data sharing on patients. HIPAA standards do not specifically prohibit sharing of electronic data between a patient's providers, but similar to 42 CFR Part 2, these limits have not been adequately clarified by the courts. State regulations created during the paper era may also present challenges to substance abuse treatment programs about implementing electronic medical records. State records may not specifically permit electronic medical records or may be vague about the status of electronic versions of records as "original" records for legal purposes. For example, the Pennsylvania Department of Health rules applicable to medical records were written in the 1970s and neither permit nor prohibit electronic records, other than to encourage "automation" and microfilming (Shay, 2005). Sharing data, software, or hardware could potentially have ramifications related to the Anti-kickback Statute, which prohibits remuneration in exchange for referrals, or the Stark referral rules on compensation relationships (Shay, 2005). The field continues to struggle with tension between providers' need to know about patients' addiction and regulations that limit information transfer.
The role of health care systems in health IT improvements
The unsystematic health care "system" in the United States bears a portion of the blame for the lack of health IT, yet public health potentially could gain enormously from the expansion of health IT into substance abuse treatment (Budrys, 2005). Treatment systems are typically comprised of policies and structural resources which, through system qualities (e.g., efficiency) and other moderating factors (e.g., case mix), can impact population health (Babor, Stenius, & Romelsjo, 2008). Despite the disparate policies and program structures within publicly-funded substance abuse treatment systems, policymakers, consumers, and other stakeholders require these systems to document that services are appropriate, effective, and cost-effective (McCarty et al., 2008), and there is a great need to identify substance abuse-related indicators in population health (Babor et al., 2008). Although U.S. states' administrative data systems provide a rich source of information regarding services delivered, state information systems are often underutilized, poorly maintained, and underdeveloped (Institute of Medicine, 1993). A foundational barrier lies in the challenges inherent in a fragmented health care system, the absence of a single mechanism for health information exchange within the treatment system, and the inability to link and share information or data, which impedes the quality of health care (Budrys, 2005; Detmer, 2003). The U.S. federal government is addressing these gaps in health IT across medical care through the American Recovery and Reinvestment Act of 2009 (ARRA), and the associated Health Information Technology for Economic and Clinical Health Act (HI-TECH), which authorized $20 billion to contribute to the development of health IT infrastructure. The laws created a national office to coordinate health IT programs, endorse standards, establish a national health IT research center to aid providers, and address the Federal Health IT Strategic Plan. HI-TECH also designates payment incentives for providers who demonstrate "meaningful use" of electronic health records; although federal officials have not yet finalized standards of "meaningful use" or procedures for payment incentives (Raths, 2009), ARRA-HI-TECH may provide opportunities for substance abuse treatment agencies to increase their capacity to measure, track, report, and monitor data (Torrey, Finnerty, Evans, & Wyzik, 2003).
A particular challenge regarding health care systems is cross-system coordination. Individuals with substance abuse problems often have other co-occurring problems during the course of recovery, including mental health problems, acute physical events (e.g., accidents), and chronic medical problems (e.g., diabetes). Waegemann's (1996) model of levels of medical records has at its pinnacle a complete electronic medical record encompassing medical and non-medical health information (e.g., exercise history) that can be used both longitudinally and across organizations; Wisdom et al.'s (under review) pilot study applying this model to a sample of substance abuse treatment agencies found that none had capability to communicate electronically with any programs outside their program. Clients are mobile, however, and do not always return to the same treatment programs, which limits the interoperability of data. Claims data from payors that obtain services from multiple treatment programs (e.g., criminal justice, health plans) are a possible source of population data for individuals with substance abuse problems. Despite the benefits of administrative data, such as its availability, low expense, and the potential for large population coverage, these data also have drawbacks in that they allow limited insight into the quality of processes of care, errors of omission or commission, and the appropriateness of care, and they frequently are inaccurate or incomplete (Iezzoni, 1997).
Health IT improvements to substance abuse treatment programs and systems can have substantial benefits for population health. Infrastructure improvements can lay the foundation for states and providers to develop surveillance systems that identify changing trends or new drugs of abuse (Cicero, Inciardi, & Munoz, 2005), performance feedback reports (Andrzejewski et al., 2001; McCarty, 2007; Wells and Johnson, 2001), and continuous outcome monitoring (Brown, Topp, & Ross, 2003; McLellan, McKay, Forman, Cacciola, & Kemp, 2005), or implement pay for performance systems (Bremer, Scholle, Keyser, Houtsinger, & Pincus, 2008; McLellan et al., 2008). The research community also has an important role in supporting innovative, system level, analytical approaches to monitor the impact of drug and alcohol treatment in contributing expertise to frame appropriate questions to be addressed with improved health IT, develop performance indicators, support data analysis, and report data for broad consumption (Rush, Corea, & Martin, 2009). In partnership with state administrators, researchers are leveraging technology to link client information (e.g., link a substance abuse treatment episode with other healthcare services) across state databases (Hser & Evans 2008; Campbell, 2009; Campbell, Deck, & Krupski, 2008). The ability to link records across systems is an important source of information to evaluate the impact of treatment on longitudinal treatment outcomes (Alterman, Langenbucher, & Morrison, 2001; Babor et al., 2008; Evans et al., 1993).
Barriers and facilitators to the implementation of health information technology
Substance abuse treatment has been slow to integrate health information technology into its administrative and clinical functions (Ducharme et al., 2005; McLellan, Carise & Kleber, 2003) despite the promise of technology to streamline administrative practices and improve the quality of care. The selection of the right technological fit for the agency may be a barrier to implementation because substance abuse treatment programs may have difficulty locating health IT that is appropriate to their program size, services provided, and needs (Wisdom, Ford, Wise, Mackey, & Greene, under review). A sociotechnical perspective to the adoption of health IT implementation combines the social aspects of system development (i.e., recognizing the skills and work of health care professionals) with technical system functioning (i.e., technology and tasks) to address how health IT fits within the organizational, operational, and cultural processes to enhance the delivery of care (Berg, 1999). The process starts with a determination of the functionality of a health IT system (i.e., billing only or integrated billing, scheduling and clinical notes) which is combined with a user-centered approach to identify the needs and requirements of the end users. A user-centered approach focuses not only on communication and training but should involve end-users early on and continuously through the design and implementation process (Berg, 1999). In the U.S., it is hoped that recently increased federal support for health IT (e.g., HI-TECH legislation and regional health IT research centers) will provide assistance in these areas to behavioral health programs.
Emerging technology facilitates the implementation of health IT, but cost remains the greatest barrier to widespread adoption (Jha et al., 2009). Frieden and Mostashari (2008) suggest that increased implementation and use of electronic records will require changes to workflows, increased emphasis on preventive care, retraining or hiring staff, and increased financial incentives to report and improve performance. The top predictors of organizational information technology adoption are: support from top management, external pressure to improve performance, the professionalism of the information technology unit, and access to external information sources (Jeyaraj et al., 2006). We discuss barriers of most relevance to implementation of health IT in substance abuse treatment below.
Cost and financial incentives
The median number of clients in outpatient substance abuse treatment programs is 48 (SAMHSA, 2008); the upfront costs of implementing either computerized assessment/treatment programs or electronic medical records for such modest numbers would be prohibitive for many programs without external funding. Data are typically collected solely to complete reporting requirements (McLellan et al., 2003), and program use of data for quality improvement or aggregate reporting is usually low (Wisdom et al., 2006). Further, financial benefits of health IT may not accrue to the substance abuse treatment program; rather, increased efficiency in electronic medical records may lower reimbursement (Jha et al., 2009). Interoperability is an additional challenge: evidence suggests substance abuse treatment programs do not have interoperable systems incorporating patient clinical, financial, and medical records within programs, and almost no interoperability between substance abuse treatment programs (Wisdom et al., 2006). Finally, metrics and methods to successfully evaluate the effectiveness and cost-effectiveness are not clear (Healthfield et al., 1998). Substance abuse treatment programs are unlikely to dramatically increase their information sharing across programs, substantially reducing a primary benefit of health IT and further reducing cost-effectiveness.
Nature of substance abuse treatment
Substance abuse treatment is fundamentally different from hospital-based care, which limits the applicability of many current health IT applications. Substance abuse treatment is typically provided in smaller programs with much lower use of technology (e.g., no radiology, low use of laboratory findings or medications) (SAMHSA OAS, 2007). Further, providers are typically master's level or bachelor's level providers--42% of counselors and 10% of support staff have a master's degree or higher (McCarty et al., 2007)--whose lower reimbursement rates also limit the cost-effectiveness of large-scale implementation. In addition, unlike many hospital-based systems, substance abuse treatment programs manifest little evidence of written protocols that describe patient flow and administrative practices. A pilot study of eight substance abuse treatment programs, for example, found few written protocols that described the flow of documentation, governed the use of program information, or outlined procedures for error checking to ensure data accuracy (Wisdom et al., under review). This lack of protocols likely would increase burden to the workflow modifications required for implementing electronic medical records; programs without established protocols will need to create protocols at the same time they are mapped to electronic systems. Finally, substance abuse treatment cultures often do not prioritize data management or have a population perspective on how data may improve services (McLellan et al., 2003; Wisdom et al., 2006). Differing perspectives on the value of data, for example, hampered the development of an addiction services information management system in Massachusetts; collaborative decision making, an advisory group representing project stakeholders, clear communication between the parties, formal feedback processes, and ongoing training reduced these barriers (Camp, Krakow, McCarty & Argeriou, 1992). These efforts moved programs toward a culture that permitted, if not embraced, data use for performance improvement.
Publicly-funded substance abuse treatment programs typically have high staff turnover (Knudsen, Johnson & Roman, 2003; McLellan et al., 2003) and, despite the absence of formal assessment, evidence suggests staff often have low technology skills in computer use (Wisdom et al., 2006), and information technology expertise is limited (Trivedi & Daly, 2007). Resistance to increasing health IT and modifying system practices also poses a concern: Organizational or staff focus on client treatment may be related to resisting use of limited resources for infrastructure or quality improvement (Helms, Moore, & Ahmadi, 2008; Mack, Brantley, & Bell, 2007; Trivedi & Daly, 2007). Educational bodies (e.g., those certifying substance abuse treatment providers) teach to licensing requirements and may have limited curricula addressing system and infrastructure issues.
Despite these barriers, some factors facilitate improvement of health IT in substance abuse treatment. Computer-assisted assessment and treatment tools are increasingly available, and evidence supporting their efficacy is slowly accruing (Satre, Wolfe, Eisendrath & Weisner, 2008). Organizations that provide licensing or reimbursement to substance abuse treatment programs are increasing requirements for performance reporting and accountability (e.g., Joint Commission; Substance Abuse and Mental Health Services Administration National Outcome Monitoring Standards), and substance abuse treatment-specific performance indicators have been developed and are being tested (e.g., McCorry, Garnick, Bartlett, Cotter & Chalk, 2000). Increased federal attention to health IT generally, and electronic medical records specifically, has the potential to increase data-management capacity in substance abuse treatment programs. Finally, resources such as the Institute for Healthcare Improvement and the Network for the Improvement of Addiction Treatment provide targeted instruction and collaborative interventions to substance abuse treatment programs to improve identification, tracking, and reporting of performance indicators (McCarty, Gustafson, Capoccia & Cotter, 2008).
Specific models for substance-abuse treatment health IT
Increased capacity to evaluate and describe substance abuse treatment programs' use of health IT can increase awareness and ultimately improve health IT in substance abuse treatment. Waegemann's (1996) five levels of medical records, for example, ranges from an automated medical record--which refers to a paper-based system that includes some printed and hand-completed forms--to a complete electronic medical record encompassing medical and non-medical health information (e.g., exercise history) that can be used both longitudinally and across organizations. Jha and colleagues' (2006, 2009) reports of electronic functionality in U.S. hospitals, however, assess more specific capabilities (e.g., physicians' notes, problem lists), but also include details not relevant to substance abuse treatment programs (e.g., diagnostic imaging) and omits specific assessment of critical importance (e.g., interoperability). Wisdom and colleagues (under review) suggest a pragmatic modification of Waegemann's model for application to substance abuse treatment programs that includes measures of integration of computer software, systems (e.g., billing, intake), and client records (e.g., demographics provider treatment notes); consistency of IT across program locations and levels of care; and a measure of how programs use the data they collect.
Application to private and non-U.S. substance abuse treatment
Private and public substance abuse treatment programs have some significant differences, suggesting health IT may be implemented in different ways or for different purposes. Private programs are larger, more likely to have physicians available, have more master's-level trained counselors, and are less likely to have referrals from the legal system (Roman, Ducharme & Knudsen, 2006). Research is mixed, however, on whether public or private programs are more likely to use innovative practices (D'Aunno, 2006; Roman, Ducharme & Knudsen, 2006). Private for-profit programs appear less likely to use electronic health records than publicly-funded programs, and it appears to be associated with higher requirements of publicly-funded programs to report characteristics of clients treated, services delivered, discharge status, and outcomes (Ducharme et al., 2005). Despite differences in circumstances, Pincus et al. suggested public and private substance abuse and mental health treatment providers work together to ensure improvements in the national health information infrastructure, and include substance abuse and mental health care as fully as they address general health care (Pincus et al., 2007).
Similarly, substance abuse treatment is managed differently worldwide. In some countries, substance abuse treatment is outside of the health care infrastructure (e.g., Sweden). Other countries have dramatically different policies related to drug use. The Netherlands, for example, supports injection rooms for the hygienic consumption of preobtained drugs under professional supervision in a non-judgmental environment (Kimber, Dolan, Van Beek, Hedrich, & Zurhold, 2003). The nature of policies and service use influences the need for and use of health IT. In addition, legal restrictions on sharing health information may be jurisdiction-specific, which would result in challenges providing population data. Regulatory systems may also play a role. Hospital-based services may have different reporting requirements compared to psychosocial services and usually external to health care systems (e.g. housing; public health). We could find no international comparison of infrastructure related to substance abuse treatment. The World Health Organization's Project Atlas, however, assessed mental health infrastructure in 155 of 191 member states and found that less developed countries were less likely to have national mental health policy, a national mental health budget, or facilities for severe mental health disorders.
Some international efforts have demonstrated the capability of health IT to obtain epidemiological research to improve the quality of care. A strong example is the European Monitoring Centre for Drugs and Drug Addiction, which combined data from several countries' health IT systems to create a core item set for treatment monitoring and reporting (Simon et al., 1999). This system allowed countries with existing national systems to modify their systems to capture data, and provided a framework for countries with less health IT infrastucture to install a treatment monitoring and reporting system (e.g., Ireland, Belgium, Greece). In addition, this infrastructure is expanding to include additional countries (e.g., South Africa) to allow the intercomparability of data (Parry, Pluddemann, & Myers, 2007).
An analysis of health IT for substance abuse treatment suggests that availability of computerized assessment and treatment interventions and electronic medical records is increasing for substance abuse treatment programs and that they face substantial barriers to significantly improving in using health IT. Major factors limiting progress in this field are the lack of substance abuse treatment-specific metrics, outcome measures, funding opportunities, and business case arguments that support implementing health IT. Research from health IT in medicine provides some useful data, but substance abuse treatment needs evaluation and research that (1) identifies targeted metrics for evaluating the characteristics and utility of health IT and describes outcomes including cost-effectiveness of health IT implementation; (2) identifies organizational factors that influence adoption of health IT and provides targeted interventions to facilitate skill-building and discuss how program culture can increase health IT implementation; (3) bolsters the business case for health IT through modified reimbursement practices or pay-for-performance initiatives that reward increased treatment program efficiency and effectiveness; and (4) addresses organizational "factors" to minimize cost and disruption associated with implementing health IT.
Although the literature on health IT implementation in medical facilities can provide useful insights for substance abuse treatment, its facilities' independence from medical facilities and the differing nature of its services call for new approaches to evaluating the utility, benefits, and costs of implementing health IT. In this era of healthcare reform, the time has come to develop and quickly implement a new infrastructure for tracking clinical services in substance abuse treatment. Such a system would facilitate the development of electronic health information exchanges and provide the ability to compare quality of services delivered within and across providers to increase surveillance and performance management. Increased attention to the opportunities and promise that health IT offers in substance abuse treatment--as well as policies that specifically address these technologies--can help improve services and outcomes for this vulnerable population.
AUTHORS' NOTE: Preparation of this manuscript was supported by funding from the Robert Wood Johnson Foundation (#57582). We appreciate the comments of anonymous reviewers
Alterman, A.I., Langenbucher, J., & Morrison, R.L. (2001). State-level treatment outcome studies using administrative databases. Evaluation Review, 25, 162-83.
Amarasingham, R., Plantinga, L., Diener-West, M., Gaskin, D.J., & Powe, N.R. (2009). Clinical information technologies and inpatient outcomes: A multiple hospital study. Archives of Internal Medicine, 169, 108-114.
Andrzejewski, M.E., Kirby, K.C., Morral, A.R., & Iguchi, M.Y. (2001). Technology transfer through performance management: the effects of graphical feedback and positive reinforcement on drug treatment counselors' behavior. Drug and Alcohol Dependence, 63, 179-186.
Ash, J.S., Berg, M., & Colera, E. (2004). Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. Journal of the American Medical Informatics Association, 11(2), 104-112.
Babor, T.F., Stenius, K., & Romelsjo, A. (2008). Alcohol and drug treatment systems in public health perspective: Mediators and moderators of population effects. International Journal of Methods in Psychiatric Research, 17(S1), S50-S59.
Berg, M. (1999). Patient care information systems and health care work: A sociotechnical approach. International Journal of Medical Informatics, 55, 87-101.
Bremer, R.W., Scholle, S.H., Keyser, D., Houtsinger, J.V., & Pincus, H.A. (2008). Pay for performance in behavioral health. Psychiatric Services, 59, 1419-1429.
Brown, T.G., Topp, J., & Ross, D. (2003). Rationales, obstacles and strategies for local outcome monitoring systems in substance abuse treatment settings. Journal of Substance Abuse Treatment, 24, 31-42.
Budrys, G. (2005). Our Unsystematic Health Care System. Lanham, MD: Rowman & Littlefield Publishers. Inc.
Camp, J.M., Krakow, M., McCarty, D., & Argeriou, M. (1992). Substance abuse treatment management information systems: Balancing federal, state, and service provider needs. Journal of Mental Health Administration, 19, 5-19.
Campbell, K.M. (2009). Impact of record-linkage methodology on performance indicators and multivariate relationships. Journal of Substance Abuse Treatment, 36(1), 110-7.
Campbell, K.M., Deck, D., & Krupski, A. (2008). Record linkage software in the public domain: A comparison of Link Plus, The Link King, and a "basic" deterministic algorithm. Health Informatics Journal, 14(1), 5-15.
Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W. Roth, E., et al. (2006). Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine, 144, E12-E22.
Cicero, T.J., Inciardi, J.A., & Munoz, A. (2005). Trends in abuse of OxyContin[R] and other opioid analgesics in the United States: 2002-2004. Journal of Pain, 6, 662-672.
Committee on Data Standards for Patient Safety. (2003). Key capabilities of an electronic medical record system. Joint Commission Journal of Quality and Safety, 29, 479-489.
D'Aunno, T. (2006). The role of organization and management in substance abuse treatment: Review and roadmap. Journal of Substance Abuse Treatment, 31, 221-233.
Detmer, D.E. (2003). Building the national health information infrastructure for personal health, health care services, public health, and research. BMC Medical Informatics and Decision Making, 3(1).
Ducharme, L.J., Knudsen, H.K., & Roman, P.M. (2005). Computer systems in addiction treatment programs: Availability and implications for program evaluation. Evaluation and Program Planning, 28, 368-378.
Evans, E., Grella, C.E., Murphy, D.A. & Hser, Y.I. (1993). Using administrative data for longitudinal substance abuse research. Journal of Behavioral Health Services and Research, 37, 252-271.
Frieden, T.R., & Mostashari, F. (2008). Health care as if health mattered. Journal of the American Medical Association, 299(80, 950-952.
Garg, A.Z., Adhikari, N.K.J., McDonald, H., Rosas-Arellano, M.P., Deveraux, P.J., & Beyene, J., et al. (2005). Effects of computerized clinical decision support systems on Practitioner performance and patient outcomes: A systematic review. Journal of the American Medical Association, 293, 1223-1238.
Goldzweig, C.L., Towfigh, A., Maglione, M., & Shekelle, P.G. (2009). Costs and benefits of health information technology: New trends from the literature [electronic version]. Health Affairs, w282-w293.
Healthfield, H., Pitty, D., & Hanka, R. (1998). Evaluating information technology in health care: Barriers and challenges. British Medical Journal, 316, 1959-1961.
Helms, M.M., Moore, R., & Ahmadi, M. (2008). Information technology (IT) and the healthcare industry: A SWOT analysis. International Journal of Healthcare Information Systems and Informaties, 31, 75-92.
Hillestad, R., Bigelow, J., Bower, A., Girosi, F., Meili, R., Scoville, R., et al. (2005). Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Affairs, 24(5), 1103-1117.
Hser, Y.I., & Evans, E. (2008). Cross-system data linkage for treatment outcome evaluation: Lessons learned from the California Treatment Outcome Project. Evaluation and Program Planning, 31(2), 125-35.
Iezzoni, L.I. (1997). Assessing quality using administrative data. Annals of Internal Medicine, 127, 666-674.
Jeyaraj, A., Rottman, J.W., & Lacity, M.C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21, 1-23.
Jha, A.K., Ferris, T.G., Donelan, K., DesRoches, C., Shields, A., Rosenbaum, S., & Blumenthal, D. (2006). How common are electronic medical records in the United States? A summary of the evidence. Health Affairs, 25(6), w496-w507.
Jha, A.K., DesRoches, C.M., Campbell, E.G., Donelan, K., Rao, S.R., Ferris, T.G., et al. (2009). Use of electronic medical records in U.S. hospitals. New England Journal of Medicine, 360, 1628-1638.
Kaushal, R., & Bates, D.W. (2001). Computerized Physician Order Entry (CPOE) with Clinical Decision Support Systems (CDSSs). In K.G. Shojania, B.W. Duncan, K.M. McDonald, & R.M. Wachter (Eds.), Making Health Care Safer: A Critical Analysis of Patient Safety Practices (AHRQ Publication No. 01-E058). Rockville, MD: Agency for Healthcare Research and Quality.
Kerr, E.A., Smith, D.M., Hogan, M.M., Krein, S.L., Pogach, L., Hofer, T.P. et al. (2002). Comparing clinical automated, medical record, and hybrid data sources for diabetes quality measures. Joint Commission Journal of Quality Improvement, 28, 555-565.
Kimber, J., Dolan, K., Van Beek, I., Hedrich, D., & Zurhold, H. (2003). Drug consumption facilities: An update since 2000. Drug and Alcohol Review, 22, 227-233.
Knudsen, H.K., Johnson, J.A. & Roman, P.M. (2003). Retaining counseling staff at substance abuse treatment centers: Effects of management practices. Journal of Substance Abuse Treatment, 24(2), 129-135.
Koppel, R., Metlay, J.P., Cohen, A., Abaluck, B., Localio, A.R., Kimmel S.E., et al. (2005). Role of computerized physician order entry system in facilitating medication errors. Journal of the American Medical Association, 293, 1197-1203.
Kramer, T.L., Owen, R.R., Cannon, D., Sloan, K.L., Thrush, C.R., Williams, D.K., et al. (2003). How well do automated performance measures assess guideline implementation for new-onset depression in the Veterans Health Administration? Joint Commission Journal for Quality and Safety, 29, 479-489.
Mack, D., Brantley, K. M., & Bell, K.G. (2007). Mitigating the health effects of disasters for medically underserved populations. Journal of Healthcare for the Poor and Underserved, 18, 432-442
McCarty, D. (2007). Performance measurement for systems treating alcohol and drug use disorders. Journal of Substance Abuse Treatment, 33, 353-354.
McCarty, D., Fuller, B.E., Arfken, C., Miller, M., Nunes, E.V., Edmundson, E., et al. (2007). Direct care workers in the National Drug Abuse Treatment Clinical Trials Network: Characteristics, opinions, and beliefs. Psychiatric Services, 58(2), 181-190.
McCarty, D., Gustafson, D., Capoccia, V.A., & Cotter, F. (2008). Improving care for the treatment of alcohol and drug disorders. Journal of Behavioral Health Services and Research, 36(1), 52-60.
McCarty, D., Kunkel, L., & Campbell, B. (2009). Confidentiality. In G. L. Fisher, & N. Roger (Eds.), Encyclopedia of Substance Abuse Prevention, Treatment, and Recovery (pp. 225-228). Thousand Oaks, CA: Sage.
McCorry, F., Garnick, D.W., Bartlett, J., Cotter, F., & Chalk. M. (2000). Developing performance measures for alcohol and other drug services in managed care plans. Joint Commission Journal on Quality Improvement, 26(11), 633-643.
McLellan, A.T., Carise, D., & Kleber, H.D. (2003). The national addiction treatment infrastructure: Can it support the public's demand for quality care? Journal of Substance Abuse Treatment, 25, 117-121.
McLellan, A.T., Kemp, J., Brooks, A., & Carise, D. (2008). Improving public addiction treatment through performance contracting: The Delaware experiment. Health Policy, 87, 296-308.
McLellan, A.T., McKay, J.R., Forman, R., Cacciola, J., & Kemp, J. (2005). Reconsidering the evaluation of addiction treatment: From retrospective follow-up to concurrent recovery monitoring. Addiction, 100, 447-458.
Miller, R.H., West, C., Brown, T.M., Sim, I., & Ganchoff, C. (2005). The value of electronic medical records in solo or small group practices. Health Affairs, 24(5), 1127-1137.
Miller, W.R., Sorenson, J.L., Selzer, J.A., & Brigham, G.S. (2006). Disseminating evidence-based practices in substance abuse treatment. Journal of Substance Abuse Treatment, 31, 25-39.
National Institute of Drug Abuse. (2009). Screening for tobacco, alcohol and other drug use. Retrieved June 14, 2009, from http://www.drugabuse.gov/nidamed/screening/
Nembhard, I.M., Alexander, J.A., Hoff, T.J., & Ramanujam, R. (2009). Why does the quality of health care continue to lag? Insights from management research. Academy of Management Perspectives, 23(1), 24-42.
Parry, C.D.H., Pluddemann, A., & Myers. B.J. (2007). Cocaine treatment admissions at three sentinel sites in South Africa (1997-2006): Findings and implications for policy, practice and research. Substance Abuse Treatment, Prevention, and Policy, 2, 37.
Pincus, H.A., Page, A.E.K., Druss, B., Appelbaum, RS., Gottlieb, G., & England, M.J. (2007). Can psychiatry cross the quality chasm? Improving the quality of health care for mental and substance use conditions. American Journal of Psychiatry, 164, 712-719.
Poissant. L., Pereira, J., Tamblyn, R., & Kawasumi, Y. (2005). The impact of electronic medical records on time efficiency of physicians and nurses: A systematic review. Journal of the American Medical Informatics Association, 12, 505-516.
Raths, D. (2009). Behavioral health IT forecast: Cloudy. Behavioral Healthcare, 29, 26-27.
Roman, P.M., Ducharme, L.J., & Knudsen, H.K. (2006). Patterns of organization and management in private and public substance abuse treatment programs. Journal of Substance Abuse Treatment, 31, 235-243.
Rush, B., Corea, L., & Martin, G. (2009). Monitoring alcohol and other drug treatment: What would an optimal system look like? Contemporary Drug Problems, 36, 545-574.
Satre, D., Wolfe, W., Eisendrath, S., & Weisner, C. (2008). Computerized screening for alcohol and drug use among adults seeking outpatient psychiatric services. Psychiatric Services, 59, 441-444.
Shay, E.F. (2005). Legal barriers to electronic medical records. Physician's News Digest. Retrieved August 1, 2009, from http://www.physiciansnews .com/law/505.html.
Sidorov, J. (2006). It ain't necessarily so: The electronic medical record and the unlikely prospect of reducing health care costs. Health Affairs, 25(4), 1079-1085.
Simon, R., Donmall, M., Hartnoll, R., Kokkevi, A., Ouwehand, A.W., Stauffacher, M., et al. (1999). The EMCDDA/Pompidou group treatment demand indicator protocol: A European core item set for treatment monitoring and reporting. European Addiction Research, 5, 197-207.
Substance Abuse and Mental Health Services Administration Office of Applied Studies. (2008). National Survey of Substance Abuse Treatment Services (N-SSATS): 2007 Data on Substance Abuse Treatment Facilities. Retrieved January 23, 2010, from http://wwwdasis.samhsa .gov/07nssats/nssats2k7web.pdf.
Torrey, W.C., Finnerty, M., Evans, A., & Wyzik, P. (2003). Strategies for leading the implementation of evidence-based practices. Psychiatric Clinics of North America, 26, 883-897.
Trivedi, M.H., & Daly, E.J. (2007). Measurement-based case for refractory depression. Drug and Alcohol Dependence, 88(Suppl. 2), 561-571.
Waegemann, C.P. (1996). The five levels of electronic medical records. MD Computing, 13, 199-203.
Wang, S.J., Prosser, L.A., Bardon, C.G., Spurr, C.D., Carchidi, P.J., Kittlera, A.F., et al. (2003). A cost-benefit analysis of electronic medical records in primary care. American Journal of Medicine, 114(5), 397-403.
Wells, S.J., & Johnson. M.A. (2001). Selecting outcome measures for child welfare settings: Lessons for use in performance management. Children and Youth Services Review, 23, 169-199.
Wisdom, J.P., Ford, J.H., Hayes, R.A., Hoffman, K., Edmundson, E., & McCarty, D. (2006). Addiction treatment agencies' use of data: A qualitative assessment. Journal of Behavioral Health Services and Research, 33(4), 394-407.
Wisdom, J., Gabriel, R., Edmundson, E., Bielavitz, S., & Hromco, J. (2008). Challenges substance abuse treatment agencies faced in adoption of computer-based technology to improve assessment. Journal of Behavioral Health Services & Research, 35(2), 158-169.
Wisdom, J.P., Ford, J.H., Wise M., Mackey D., & Green C.A. (under review). Substance abuse treatment programs' data management capacity: An exploratory study. Manuscript submitted for publication.
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|Author:||Wisdom, Jennifer P.; Ford, James H., II; McCarty, Dennis|
|Publication:||Contemporary Drug Problems|
|Date:||Jun 22, 2010|
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