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Informatics 

Informatics
Chapter:
Informatics
Author(s):

Ardis Hanson

, Bruce Lubotsky Levin

, and Aimon Miranda

DOI:
10.1093/med/9780190238308.003.0013
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Introduction

Informatics, as an academic discipline, involves the practice and theory of information processing, systems engineering, and classification research and is related to the study and practice of creating, storing, finding, manipulating, sharing, and communicating information. Using theoretical frameworks and practical theory from computation, communication, and cognition, we suggest informatics is how we transform data and information to create a body of knowledge that can answer the questions we ask about populations, geographic areas, disease surveillance, and much more.

Informatics is not new. The early 1990s saw the development of electronic office management software that could address computerized medication administration records, implement pharmacotherapy standards, and document pharmacist activities (Canaday & Yarborough, 1994; Cherici & Remillard, 1993; Teich, Spurr, Schmiz, O’Connell, & Thomas, 1995; Zimmerman, Smolarek, & Stevenson, 1995). Sujansky (1998) wrote of the need for decision support tools embedded in the electronic health record to make it more than just a paper analog, as well as the use of bibliographic retrieval systems, such as PubMed, to facilitate clinical decision-making. Today, informatics hardware and software handles decision support functions, as well as in-house administrative tasks, such as billing and scheduling, and many managed care functions, such as certifications, authorizations, treatment plans, medication evaluation forms, treatment summary forms, and outcome assessments. To do so, informatics utilizes data, contextualizes information, and builds a body of knowledge.

If the primary focus of an information system is a measurement or a personal characteristic of an individual, then we are talking about data. Information is data placed within a specific context (after analysis). Information applied within specific rules or guidelines is a body of knowledge or, in a shortened form, knowledge.

Take for example the emergence of vital statistics (birth and death records) in the United States in the early 20th century. We took discrete data at the local and state levels of government and examined it in light of the effects of three public health activities—(a) immunization, (b) sanitation, and (c) nutrition—on the health of individuals and communities. The information we gleaned from viewing data in this way led to the control of infectious diseases in the United States.

Public health practice has three core functions: (a) the identification or assessment of public health problems; (b) policy development (education, partnerships, and regulation); and (c) provision of necessary services (enforcement, linkages, workforce, evaluation, and research). We suggest the essential component in each of the three public health core functions is the availability and quality of data and information to create an accurate and complete body of knowledge. However, data comes in many formats—textual, visual, geospatial, and numeric—and levels, including primary or secondary data. Textual data are reports, best practice guidelines, and state or federal regulations. Visual data may be excerpts of larger numeric or geospatial datasets. Geospatial and numeric data form the basis for statistical data.

Given the amount and types of data collected by public and private organizations, as well as by local, city, county, state, and national agencies, for various purposes, information systems remain disparate in their structure and function. Hence there is a significant need to develop high-quality data standards that provide the basis for uniform, comparable, and good-quality information on populations, disorders, and services that address the dual needs of pharmacy and public health.

International Perspective: WHO

At an international level, the World Health Organization (WHO) Programme for International Drug Monitoring is an excellent example of an integrated public health pharmacy informatics solution. Created in 1961 as a response to the global outcry about thalidomide, over 130 countries are part of the WHO’s pharmacovigilance program (WHO, 2016b). The WHO Collaborating Centre for International Drug Monitoring is the Uppsala Monitoring Centre (UMC), located in Uppsala, Sweden. The UMC screens for and analyzes international adverse-reaction data, provides current awareness services to keep health professionals up to date on the state of the science and tools, and provides technical assistance and training to establish and run national pharmacovigilance programs and UMC tools (UMC, 2000).

The UMC has created a number of tools using the WHO Global Database of Individual Case Safety Reports (ICSR). VigiBase® contains data on more than 10 million ICSRs submitted from over 100 countries since 1968 and includes conventional medicines, traditional medicines, and biological medicines (e.g., vaccines) (Viklund & Biiriell, 2015). VigiBase uses the WHO Drug Dictionary and the medical terminology classifications from WHO-Adverse Drug Reaction Terminologoy (ART), the International Classification of Diseases (ICD), and the Medical Dictionary for Regulatory Activities (MedDRA) to provide standardized and/or crosswalked nomenclature and ontologies.

Standardized Nomenclature

The importance of standardized and/or crosswalked nomenclature and ontologies is critical, especially when working with specialized vocabularies across multiple language groups, medical models, and/or cultural frames, at the global, national, and state levels. The WHO Drug Dictionary Enhanced, for example, is the world’s most comprehensive source of pharmaceutical and medicinal product information. Although most of the entries are prescription-only products, the Drug Dictionary Enhanced also includes over-the-counter drugs and products, pharmacist-dispensed preparations, biotech and blood products, diagnostic substances, and contrast media. There is also an expanded version of the Drug Dictionary Enhanced, which includes the WHO Herbal Dictionary. Herbal medicines and traditional/complementary medical practices are used extensively across the globe (WHO, 2013); approximately 75% of the global population uses herbs for basic health care needs (Pan et al., 2014).

The WHO-ART is used for coding clinical information related to adverse drug reactions. It is a four-level hierarchical terminology, which begins at the body system/organ level classes used by drug regulatory agencies and pharmaceutical manufacturers.

Used in epidemiology, health management, and quality and clinical settings, the ICD is the standard global diagnostic tool (WHO, 2016a). Its diagnostic classes are used as a standard for vital statistics entries, reimbursement and resource allocation, and storage and retrieval queries for administrative data analysis and quality reviews. Translated into 43 languages, the ICD is used by all European Union (EU) member states, and most of the member states use the ICD to report mortality data.

Created by the International Council for Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (2013), MedDRA standardizes the sharing of regulatory information for medical products used by humans. MedDRA covers data on pharmaceuticals, biologics, vaccines, and drug-device combination products from the clinical phase 1 to marketed product phase IV. Used by regulatory agencies, public health officials, medical researchers, and the drug industry, MedDRA’s multiaxial hierarchical classification system allows signal detection and/or monitoring of clinical syndromes when symptoms encompass numerous systems or organs.

Other Tools

Other tools include CemFlow, a prototype data monitoring tool to analyze antimalarial treatment data from cohort event monitoring programs developed for use in Nigeria and Tanzania Suku (Suku et al., 2015), and PaniFlow, developed for the Swiss medicines agency Swissmedic to monitor adverse events following administration of drugs and vaccines against the influenza A (H1N1) pandemic (Viklund & Biiriell, 2015). In addition to these tools specifically developed for WHO and UMC staff collaborators, there is VigiAccessTM, a publicly accessible interface to VigiBase®. Users can find information about possible side effects of medication, gain a more global understanding of the effects of drugs on people, and learn more about drug safety (Uppsala Monitoring Centre, 2015).

Regional Perspective: SemanticHEALTH

In 2008, the WHO conducted the Public Health Informatics Key Informant Survey, which became the basis for a EU project. The 28 member states of the EU, which vary widely in size, wealth, and political systems, began the SemanticHEALTH project, which addresses sematic interoperability in health systems in the EU.

Overall interoperability is defined as

the ability, facilitated by ICT applications and systems, to exchange, understand and act on citizens/patients and other health-related information and knowledge among linguistically and culturally disparate health professionals, patients and other actors and organizations within and across health system jurisdictions in a collaborative manner. (Stroetmann et al., 2009, p. 10)

Sematic interoperability (SIOp) therefore identifies best practices in the coding, transmission, and use of meaning of health information between patients, providers, and institutions and in research and training across local, regional, and national borders. To do this, SIOp examines three key components: who delivers and receives health care (actors), how health care is delivered (processes), and the relationship of the actors and processes within the context of existing national health policy frameworks (laws, regulations, stakeholders) and infrastructures (institutional, technological, and service systems).

Semantic HEALTH emphasizes the importance of standardization in clinical data to ensure semantic interoperability. It identified as key the following three items: (a) generic reference models for clinical data from international standards organizations, (b) agreed-upon clinical data structure definitions, and (c) clinical terminology systems, such as Logical Observation Identifiers Names and Codes (LOINC) and Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT).

The generic reference models were ISO/EN 13606 Part 1, openEHR, and HL7 Clinical Document Architecture. A reference model is an abstract framework or domain-specific ontology, which consists of an interlinked set of clearly defined concepts, objects, and relationships. Reference models can create standards for software or for a health record. Reference models also can help establish clear communication, such as “this” = “that,” if one is working across different terminologies or languages. They help clinical specialists define the semantics of clinical information systems or transmission protocols for online health exchanges. All three create archetypes, (e.g., machine-readable specifications of how to store patient data), which are then used to provide true analytic functions, such as decision support, and allow complex queries. The ISO/EN 13606 Part 1 standard defines a means by which computer systems can exchange health care records with each other (Austin, Sun, Hassan, & Kalra, 2013) while Part 2 defines how archetypes should be formally represented (Tapuria, Kalra, & Kobayashi, 2013). Similarly, the openEHR Foundation publishes specifications for building “clinical models,” or archetypes.

Health Level Seven (HL7) refers to a set of international standards for the transfer of clinical and administrative data between software applications used by various healthcare providers (Hanson & Levin, 2013). HL7 is one of several American National Standards Institute–accredited standards developing organizations (SDOs). Since the HL7 SDO develops standards for interoperability across platforms and data, this enhances the ability to share data, reduce ambiguity, improve care, and optimize workflow (Dolin, Rogers, & Jaffe, 2015). The HL7 is one of the standards used by the U.S. National Health Information Infrastructure (NHII).

National Perspective: CDC, Informatics, and Surveillance

The NHII was a health care standardization initiative to develop an interoperable, health information technology (HIT) system for the United States. The NHII report addressed three dimensions of health information: personal health, the health care provider, and population health (National Committee on Vital Health Statistics, 2001). Today, the NHII is called the Nationwide Health Information Network (NwHIN), sponsored by the Office of the National Coordinator for Health Information Technology. The NwHIN focuses on standards, services, and policies that enable secure health information exchange over the Internet (Office of the National Coordinator, 2013).

Perhaps the best-known public health organization in the United States working in informatics is the U.S. Centers for Disease Control and Prevention (CDC). The CDC’s Division of Health Informatics and Surveillance provides access to a number of public health information and surveillance systems of interest in public health pharmacy. The National Syndromic Surveillance Program uses almost “real-time” prediagnostic data to detect and identify unusual disease or hazardous event activities for further investigation or response. The BioSense application is an electronic health information system with standardized tools and procedures to facilitate the collection, sharing, and analyzing of health data.

Take the case of mosquito-borne viruses, an increasing public health concern in the subtropical continental United States and in Puerto Rico. In 2010, the CDC identified an increase in the number of persons seeking care at Veterans Administration facilities who showed dengue-like signs and symptoms (Schirmer et al., 2013). The dengue virus is a leading cause of illness and death in the tropics and subtropics. In 2014, in Florida, the CDC confirmed 24 cases of dengue fever and 18 cases of a new mosquito-transmitted virus called chikungunya. To date, the presence of the chikungunya virus is confirmed in 44 countries in the Americas, with over 1 million suspected cases (Sharp et al., 2014).

The CDC National Notifiable Diseases Surveillance System Modernization Initiative (NNDSS NMI; 2014) is a surveillance system for the collection, analysis, and sharing of health data. The NNDSS NMI is standardizing the nation’s health data and exchange systems. Two components of the NMI that are of especial interest to individuals working in public health/pharmacy are the development of new HL7 Message Mapping Guides (MMGs) and the CDC Message Validation, Processing, and Provisioning System (MVPS). The MMGs provide a structure for case notifications to the U.S. Public Health Information Network, using messaging standards and specifications, vocabulary standards and value sets, and business rules (CDC, 2012). Each MMG is designed for a specific disease event. The MVPS receives, processes, and manages the data for all nationally notifiable diseases based on HL7 standards (CDC, 2014). Since the MVPS is compliant with the HL7 standards, the amount of data that can be processed and the increased granularity of the data that can be processed should enhance the CDC’s ability to surveil, identify, and respond to disease and/or hazardous events.

The CDC’s Electronic Laboratory Reporting (ELR) is another public health priority. Clinical laboratory test results that are not uniformly coded or documented in a standardized manner across multiple standards are problematic. Part of the Centers for Medicare & Medicaid Services’ Medicare and Medicaid Programs Electronic Health Records Incentive Program (Lamb et al., 2015), the intent of the ELR is to encourage professionals and providers participating in Medicare and Medicaid to adopt, implement, and sustain use of certified electronic health records (EHR) technology in the delivery of care.

Although many ELR and EHR systems uniformly code to the International Classification of Diseases, 9th revision Clinical Modification: Current Procedural Terminology and National Drug Codes, few ELR systems consistently use LOINC, which is used by the U.S. Food and Drug Administration (U.S. FDA) and the Clinical Data Interchange Standards Consortium (Hanson & Levin, 2013). The CDC incorporated LOINC into its Reportable Condition Mapping Tool (RCMT). The RCMT, also HL7 compliant, maps between reportable conditions, their associated LOINC laboratory tests, and SNOMED CT. SNOMED CT addresses clinical findings, symptoms, diagnoses, procedures, body structures, organisms and other etiologies, substances, pharmaceuticals, devices, and specimen. More importantly, SNOMED CT crosswalks to other international standards and classifications, which is an important consideration for an increasingly global health perspective (McBride, Lawley, Leroux, & Gibson, 2012). Consider that even English changes as one moves across English-speaking countries. There are guides to understand medical terminology in every country. Consider health care provided in Australia and New Zealand. Not only do these guides provide Australian terminology and sociocultural perspectives common in the Australian health care environment (Walker, Wood, & Nicol, 2012), but they also provide frameworks for standard naming conventions and terminology to accurately describe medications.

Pharmacy Practice

Technology to support pharmacy practice is not new. The use of technology in pharmacy in the United States dates back to the 1970s and possibly earlier. In 1992, an expert conference arranged by U.S. Pharmacopeia predicted that, by 2020, the future of pharmacy practice would be affected by three factors: movement from a local to a global market, achieving cost-effectiveness in health care, and benefits from information technology (Bezold, Halperin, & Eng, 1993). More than 20 years later, another international study on pharmacy practice found that the vision for community pharmacy would have a strong patient care orientation and equally strong support from technology, including through work optimization, online documentation and follow-up, access to evidence-based practice and decision-making, shared patient data, and e-prescribing (Westerling, Haikala, & Airaksinen, 2011). These predictions and visions have come true. These did not only affect community pharmacy but almost all areas of pharmacy such as institutional, ambulatory care, and hospitals. There has even been the rapid development of an area of pharmacy informatics to meet major national and international needs and goals. Technology continues to affect many aspect of public health pharmacy, such as mobile health (m-health), electronic health records, and e-prescribing.

m-Health

M-health uses mobile technologies as tools and platforms for health research and health care delivery (Fogarty International Center, 2016). This is a broad area that can include the most commonly used item such as smartphones (i.e., iPhone, Android) and tablets (i.e., iPad, Surface) to the more advanced items such as wireless medical devices (i.e., blood pressure cuffs, glucometers) via Bluetooth, medical mobile applications (apps), and sensors linked to pacemakers. Different platforms of social media (i.e., Twitter, Instagram) and simple short messaging services can be utilized in m-health and have been to achieve different outcomes. Currently there is limited literature related to m-health as compared to other areas of pharmacy informatics, but the existing literature has been positive.

Text messaging, an early form of m-health, can be used to facilitate behavior change and improve health (Clauson, Elrod, Fox, Hajar, & Dzenowagis, 2013). A systematic review found text messaging to be effective in terms of addressing diabetes self-management, weight loss, physical activity, smoking cessation, and medication adherence (Hall, Cole-Lewis, & Bernhardt, 2015). Especially promising is its accessibility in developing nations as mobile phones rather than other health infrastructures reach further into developing countries for education and awareness, remote data collection and monitoring, communication and training, and epidemic tracking (Vital Wave Consulting, 2009). In South Africa, text messaging has been used in campaigns to build awareness of HIV/AIDs, leading to an increase in contact with helplines and HIV self-testing for Project Masilueleke (PopTech, 2016). Text messaging has experienced rapid growth due to its ease of use and accessibility, but related research is still limited and is difficult to interpret due to heterogeneity of health conditions and patient populations.

Mobile medical applications, or apps, can be used in several different ways. There has been substantial growth of mobile applications with certain apps targeted for patient use or clinician use. They can be used by clinicians to help increase access to clinical information for point-of-care by allowing access to drug information references and clinical practice guidelines. This can help with workflow and increase productivity and patient quality of care. For patients, there are apps that promote patient engagement through education, data collection, and feedback. There are also apps that can be used to help improve medication adherence that can be recommended for patient use (Dayer, Heldenbrand, Anderson, Gubbins, & Martin, 2013). Although readily available and easy to use, there are issues with mobile app development. There is a lack of evidence-based information or accuracy as most apps available often lack medical references or medical professional input (Ferrero, Morrell, & Burkhart, 2013; Mosa, Yoo, & Sheets, 2012; Wolf et al., 2013). Currently, the U.S. FDA (2015) only regulates a subset of apps that meet the definition of “device” and that “are intended to be used as an accessory to a regulated medical device” or “transform a mobile platform into a regulated medical device.”. As the number of apps and popularity continues to grow, there may be future oversight needed to help regulate these apps.

Mobile technology has undergone rapid advances in the past several years with smartphones and tablets, as these devices have become a societal mainstay and their use has increased in clinical care due to the amalgamation of different functions that can used (Aungst, Miranda, & Serag-Bolos, 2015). These devices can be used to serve simpler functions for pharmacist as clinical references, ordering processing, communication, patient engagement, and documentation. Some mobile technology devices are much more advanced and are geared toward consumer use, such as wearable devices (i.e. Fitbit, Misfit Shine) that allow users to track certain biometrics such as steps, calories burned, sleep, and heart rate. Other mobile technology devices that are not wearables and available to consumers can be used to collect data such as blood pressure, blood glucose, and electrocardiograms on patients (i.e., iHealth iglucometer, Withings blood pressure cuff, and AliveCor). Many organizations are recognizing the potential benefits of incorporating such technology into practice, but the overarching issue is training health care professionals to be ready to integrate such devices into their workflow and practice (Miranda, Serag-Bolos, Aungst, & Chowdhury, 2016).

M-health is a rapidly growing area that is gaining much interest. Its major strengths are its scalability, increased reach to populations, and integrative data collection. These technologies are making the concept of telemedicine more robust. While this area seems promising, many barriers still exist, such as the need for additional research, the idea that technology changes quickly, and the integration of such technologies. Although data collection is also a strength, it can be seen as a weakness as well as there is the concept of data overload and the need to manage large enough amounts of data to make it clinically useful.

Electronic Medical Records, Electronic Health Records, and Personal Health Records

Electronic medical records (EMRs) are electronic records of health-related information on an individual that can be created, gathered, managed, and consulted by authorized clinicians and staff within one health care organization (National Alliance for Health Information Technology, 2008). EMRs are not synonymous with EHRs, which aggregate all of the data being generated by individual EMRs. Personal health records (PHRs) contain the same type of information as EHRs, such as diagnoses, medications, immunizations, family medical histories, and provider contact information, but they are designed to be set up, accessed, and managed by patients (HealthIT.gov, 2015).

Implementation of EMRs hold the promise of improved patient care, reducing medical errors, and coordinated care (DesRoches, Painter, & Jha, 2015). EMRs have several features that assist with pharmacy practice such as documentation of clinical services and patient care, medication lists, and allergy documentation and as a means of conducting clinical research. Incentives from the Health Information Technology Act of 2009 have drastically increased the adoption rate of certified EHRs. The increased use of EHRs makes it possible to capture massive amounts of clinical data that can be used in areas of data mining and data application and integration (Ross, Wei, & Ohno-Machado, 2014). Through data mining and applying predictive analytics and decision support, adverse drug events have been detected through utilizations of EHRs (Chazard, Ficheur, Bernonville, Luyckx, & Beuscart, 2011; Ji et al., 2011). This process is not proactive nor expeditious and can be used to augment the current process (Coloma, Trifiro, Patadia, & Sturkenboom, 2013).

A component of EMRs are clinical decision support (CDS) tools. CDS provides clinicians, patients, or individuals with knowledge and person-specific or population-specific information, intelligently filtered or presented at appropriate times, to foster better health processes, better individual care, and better population health (Osheroff et al., 2007). These tools have become very helpful in practice as a means to assist with clinical care, preventative care, diagnosis, and follow-up. For preventative care, they have been used to provide reminders for vaccinations and screenings. A meta-analysis found that computerized reminders increased preventative practices when these reminders were used (Shea, DuMouchel, & Bahamonde, 1996). Such practices are often missed when there are other issues that need to be addressed during the patient visit.

Although EMRs have become the center of clinical practice in a very short matter of time, there are still significant efforts being made to create a fully functioning interopertability model of health information exchange across the United States. EHRs could be used to bridge a gap between public health practice and clinical medicine. EHRs could support public health surveillance by designing standard computer algorithms to identify cases that meet surveillance case definitions and present them to public health agencies or facilitate surveillance for common chronic disease states by providing additional information on clinical parameters and risk factors (Birkhead, Klompas, & Shah, 2015).

PHRs are a relatively new area that is meant to improve patient engagement and encourage family health management. Currently, there are two types of PHR: (a) standalone/web-based and (b) incorporated/tethered (Pushpangadan & Seckman, 2015). Standalone/web-based PHRs can be printed, downloaded, or accessed via smartphones or tablets. Incorporated/tethered PHRs, also known as patient portals, are connected to EHRs, which are usually associated with a health care institution or insurance company (George & Hopla, 2015). There are several consumer concerns regarding its use, privacy, and security of these systems, leading to a slow adaption of PHRs (Pushpangadan & Seckman, 2015). This may change as time progresses due to millennials and baby boomers wanting online access to their health information through the use of patient portals.

To Err Is Human: Addressing Medication Errors through Technology

One of the best-known instances of the use of technology to improve pharmacy practice came about with the Institute of Medicine’s (IOM) publication of To Err is Human: Building a Safer Health System (Kohn, Corrigan, & Donaldson, 2000), which addressed the increased mortality and morbidity in American hospitals due to medication errors. Three main causes of medication errors are similar-sounding drug names (e.g., Celexa for depression and Celebrex for arthritis), illegible physician handwriting, and the continued expansion of the drug universe, that is, the number of new drugs and the number of prescriptions filled annually.

The IOM report recommended a number of changes, such as workplace changes to improve medication safety practices, standardization of terminology, minimizing data handoffs, and creating protocols and checklists. A year later, a second IOM (2011) report, Crossing the Quality Chasm: A New Health System for the 21st Century, called for the automation of patient health records, which could be integrated with computerized prescriber-order-entry (CPOE) systems, drug distribution systems, and medication administration systems.

By 2003, the Medicare Prescription Drug, Improvement, and Modernization Act required that Part D plans support an “electronic prescription program” (Bell & Friedman, 2005). By 2005, Medicare had issued a number of technical standards for these systems. However, subparts of existing standards that were used rarely, such as the fill status notification feature of the NCPDP SCRIPT standard, needed to be tested. Other possible standards were identified, such as RxNorm, created by the National Library of Medicine, which creates crosswalks across similar generic and brand-name drugs to their active ingredients, components, and dose forms (Hanson & Levin, 2013).

Reengineering Versus Automation

Automating patient records and CPOE systems present an interesting example of the use of reengineering and automation in patient care and pharmacy services. Re-engineering is determining how a system performs and deciding how to change the system to make it perform more effectively. Automation is the act of replacing human work with work done by machines. So, when we talk of re-engineering, the topic under discussion can range from re-engineering pharmacy layout to become more productive to re-engineering the medication error-reporting process to re-engineering biosimilar drugs. However, re-engineering and automation are not always distinct form the other. Automation of a workflow process is a key component for many re-engineering initiatives.

Automation Issues for Formularies

Automation and re-engineering can play an important role in addressing formulary management. Traditionally, a formulary was a collection of formulas used to compound and test medication. Today, a formulary is an authoritative list of prescription drugs and drug products available to enrollees approved for use by a hospital, health system provider, or a state/national health service. Basic information in a formulary database generally includes the names of the discrete therapeutic entities and commercially packaged drug products, a unique identifier (e.g., a National Drug Code, the names of the drugs contained in a drug product, administration protocol, side effects, contraindications, strength and dosage range, and price).

A national formulary contains a list of medicines approved for prescription throughout the country. According to the WHO (2015), over 156 countries now have national lists of essential medicines, with separate lists for adults and for children. Two examples of national formularies are the U.S. Centers for Medicare and Medicaid Services and the British National Formulary managed by the National Health Service.

Automated pharmacy systems and pharmacy information system formularies need automatic and synchronous updates to ensure accuracy across related purchasing, inventory, and dispensing systems. However, there are a number of problems related to formulary interoperability, including formulary-database synchronization and syntactic and sematic interoperability (McManus et al., 2012).

In a study on two American Society of Health-System Pharmacists Section of Pharmacy Informatics and Technology webinars, participants voiced their frustration with the lack of synchronization among their formularies, the EMR, and “downstream” systems (Volpe et al., 2014). The inability to synchronize formularies with CPOE, e-prescribing systems, inventory and purchasing systems, automated dispensing cabinets, and unit dose robots, to name just a few, means pharmacists spend a significant amount of time doing manual entry and maintenance across numerous systems. Seventy-eight percent of participants did manual entry; almost half of participants reported spending 10 or more hours a week to keep the formulary data updated, and 51% reported they managed between five to nine online systems to maintain current and updated formulary data (Volpe et al., 2014).

Why are these issues problematic? Brookins et al. (2011) argue that manual synchronization is a major challenge for formularies. Manual entry increases risk of data-entry error, is labor and personnel-intensive, may not reflect actual time-dependent product or activity changes, and result in less accuracy for downstream automated systems. Further, with the increased reliance on robotics and compounding devices, new data requirements will emerge specific to these new tools and applications to ensure patient safety, reduce prescribing error, and manage costs. Working with large, distributed, aggregated data sets also presents a significant challenge for integrated HIT and health information exchange systems, especially in the normalization of formulary data collected over time or across institutions (Wynden et al., 2011). Such data is reused in clinical translational research, administrative data/service utilization review across health systems, or comparative effectiveness reviews.

Challenges in Re-engineering

Re-engineering procedures and processes is much more than figuring out how to improve a process. The success of re-engineering often depends on a number of factors: financial, workforce, strategic, cultural, structural, technological, and security. Financial factors, such as the cost of implementing HIT systems and potential liabilities in implementation and maintenance of HIT systems, often limit the use of these systems. Existing IT workforce may not be able to address the added complexity of implementing and integrating HIT systems. Organizations may not have incorporated HIT into their long-range strategic or operational plans, which would affect all of the factors named previously. Workplace culture may not be primed to adapt successfully to new ways of working in addition to new technology. Structurally, technology, as in hardware, is inherently problematic, with interoperability and cross-platform migration issues to address. Operating new technologies is a major technological challenge. The more complex the HIT, the less likely intuitive “click here” buttons or easy-to-use “plug and play” modules will be available. It is difficult for users to change roles or workflow processes, especially with new systems to learn and use.

Finally, security in HIT and health service delivery in the United States focuses on privacy and confidentiality framed by the Health Insurance Portability and Accountability Act (HIPAA) standards. However, since federal HIPAA legislation may be preempted by more rigorous state standards, there are hindrances to interstate flow of patient information that cannot be easily resolved.

Pharmacovigilance

Pharmacovigilance, defined as the detection, assessment, and prevention of long- and short-term adverse effects of medicines, is part of postmarketing product surveillance, which tracks drugs, appliances, and devices postrelease to the public. Pharmacovigilance is very important from a global perspective. There may be significant differences in the occurrence and frequency of adverse drug reactions and drug-related problems between countries and within regions within countries. This may be due to many factors, such as pharmaceutical quality and composition of locally produced pharmaceutical products or the use of nonorthodox drugs (e.g., traditional or indigenous remedies). Additional factors also include access to and distribution of drugs, drug use (e.g., indications, dose, availability), and sociocultural factors, such as genetics, diet, and traditions.

An interesting study on the use of VigiBase® was the identification of substandard medicines. Substandard medicines have the potential to pose a significant threat to patient safety and community health. In 2008, the CDC began an investigation of rapid-onset, acute, and severe adverse drug reactions (ADRs) to injected heparin sodium; many of the ADRs had been fatal (Blossom et al., 2008). In addition to the U.S. cluster, countries in the EU and Asia reported ADRs with heparin products from different manufacturers. It was determined that a heparin supply in China had been contaminated with a semisynthetic oversulfated chondroitin sulfate and distributed (Liu, Zhang, & Linhardt, 2009). However, this ADR emphasized the complexity of monitoring and analyzing polydispersed, polycomponent, polypharmacologic agents (Liu et al., 2009).

In 2011, the UMC developed a novel algorithm for VigiBase to identify reporting patterns that may indicate substandard medicines (Juhlin et al., 2015). The algorithm was successful in identifying historical clusters; however, the researchers concluded there needed to be additional data reported at the national level and processes created to allow the algorithm to be truly useful. Additional data needed include detailed information on the product and its distribution channels, samples of suspected products for analysis, and laboratory capacity to analyze suspected samples at the national level (Juhlin et al., 2015).

Implications for Public Health and Pharmacy Practice

There are a number of challenges when we examine public health pharmacy informatics, such as integrating surveillance systems, improving data mining, enhancing online analytical processing, standardizing public health vocabulary and classification systems, addressing emerging automated coding systems and legacy systems, and workforce development. These are in addition to other technological solutions in the workday of a pharmacist.

Accurate transmission, encryption, reception, and storage of client information and public health data are critical to ensure appropriate treatment decisions. However, existing legislation and professional rules of conduct often hinder implementation of informatics projects and programs. As legislation plays “catch up” with the advances in technology, there is great uncertainty about the application of existing legislation to HIT and health information exchange, what actions constitute statutory violations, or what actions create litigation risks. There is no easy solution, as shown by the number of case law decisions dealing with antitrust concerns, fraud and abuse, malpractice, state licensing and credentialing, and anti-kickback laws.

One of the areas for which informatics looks promising in pharmacy practice is the management of vulnerable older inpatients on high-risk medication or polypharmeutical regimens. Another area is identifying discrepancies in EMRs through pharmacist medication reconciliation. A third area is continuity of care in transitional care settings, as patients are shifted from one health care setting or service to another.

However, there are also a number of concerns as public health pharmacy continues to rely on technology. One major challenge is integrating patient care services into current and emerging business models. Some pharmacists may decide to base their practice on dispensing and sales; others may focus on services provision. Each of these has different implications for public health pharmacy practice.

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