ORIGINAL PAPER

Using Multiple Array Sensing and Non-Invasive DataCapture As a Model For Polypharmacy Error Detection

Daniel Lorence & James Li

Received: 16 November 2010 /Accepted: 16 February 2011 /Published online: 13 May 2011# Springer Science+Business Media, LLC 2011

Abstract Developing standards and technology modelsthat will facilitate e-prescribing is one of the key actionitems in the federal government’s plan to build a nationwideelectronic health information infrastructure in the UnitedStates. E-prescribing has the potential to drive change in thehealthcare industry, but the unavailability of diagnostictesting and detection equipment outside of clinical settingsmakes expanded collection and use of information prob-lematic. Most solutions are provider-based, and limited byorganization-wide startup & maintenance costs, and risk-averse data distribution policies. Objective, consumer-provided standardized data can facilitate the use ofdistributed information networks in polypharmacy detectionand avoidance. In this technology review we propose hereone promising model for polypharmacy management andintegrated diagnostics through the use of breath-based,multiple array sensing and data capture.

Keywords Multiple array . Sensing . Non-invasive . Datacapture . Polypharmacy .Medical error . Detection

Introduction

Efforts to modernize the American health care system haveaccelerated over the last 5 years due in part to severallandmark studies revealing the toll of medication errors. In1999, the Institute of Medicine (IOM) estimated that asmany as 7,000 people died each year from medicationerrors alone, accounting for 1 out of 131 ambulatory deaths

[1]. Another study by the Center for Information Technol-ogy Leadership showed that 8.8 million adverse drugevents (ADEs) occur each year in ambulatory care. Inhospitals, the average patient is subject to at least onemedication error per day [2]. This study also revealed thatfully one quarter, or 3 million, of these errors were“preventable”[1].

According to CMS in its proposed rule (Federal Register2005; 42 CFR Part 423: 6260), preventable ADEs occur-ring in hospitals cost the American health care system $3.5billion per year. Testimony before the National Committeeon Vital and Health Statistics (NCVHS)indicates that ADEsin ambulatory settings amount to upwards of $887 million.Aside from the significant problem of illegible handwriting,the current paper-based system for recording and commu-nicating drug prescriptions in the United States is a poormedium of communication and is associated with ineffi-cient workflows [4]. Industry testimony before the NCVHSalso indicates that almost 30% of prescriptions requirepharmacy callbacks, resulting in 900 million prescription-related telephone calls annually. To address these concerns,scholars, health experts, and industry leaders have sup-ported the switch from paper to an electronic system ofprescribing. Electronic prescribing, also known as e-prescribing, eliminates incorrect handwriting interpretation,ensures that vital fields include meaningful and relevantdata [2, 3], and is available to the physician at the point ofcare.

Electronic prescribing also enables the delivery of clinicaldecision support (CDS) including formulary checks, checksfor allergies, drug-drug interactions, unusually high doses,and clinical conditions, as well as suggestions for appropri-ate dosages. In addition, in its final e-prescribing rule (70 FR67568) CMS noted that experts predict a reduction in errorswhen physicians send medication orders to pharmacies

D. Lorence (*) : J. LiPennsylvania State University,University Park, PA, USAe-mail: danlor467@yahoo.com

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electronically. All told, e-prescribing can help avoid morethan 2 million ADEs annually, of which 130,000 are life-threatening [2, 5].

In terms of monetary savings, e-prescribing has thepotential to make a profound impact. In addition toreducing the aforementioned nearly $3.5 billion spentannually on ADEs, e-prescribing could also generatesavings by improving providers’ ability to make moreinformed decisions about appropriate and cost-effectivemedications. According to the AHRQ reporting on theCenter for Information Technology Leadership [7], anadditional cost savings of $2.7 billion would result frome-prescribing’s ability to reduce clinicians’ phone time.CMS also reports that the e-Health Initiative recentlyestimated that widespread adoption of e-prescribing couldsave the United States healthcare system $27 billion peryear. However, e-prescribing is much more than the simpleelectronic transmission of a prescription between prescriberand pharmacy. E-prescribing can also enable significantimprovements in patient safety, quality of care and costeffectiveness. On a practical level, e-prescribing representsjust one part of a complete clinical strategy and at itshighest functioning level, e-prescribing solutions form partof a complete medication record, both leveraging andadding to data captured during other clinical processes.

The e-Health Initiative outlines how prescribers (physi-cians and others who have authority to write prescriptions)make their prescribing decisions using whatever medical,medication, and eligibility information is known or avail-able to them. Typically, they give a handwritten prescrip-tion to the patient or phone or fax it to the dispenser (thepatient’s pharmacy of choice). Prescribers also may usetheir computers to send faxes to dispensers either directly orthrough an e-prescribing network. At the dispensing site,tasks are somewhat more automated. Through internal andexternal electronic claims, eligibility, and benefits verifica-tion processes, the dispensing pharmacist may identifycontraindications, lower-cost alternatives, or the need forprior authorization. At any step in the process, the dispensermay need to contact the prescriber by phone for clarifica-tion or approval of change. Dispensers also must frequentlycall the prescriber to obtain approval for refills or renewalswhere they are not specified on the prescription or whenthey have run out.

The e-Health Initiative further argues that prescribersmay not have access to the latest drug information, or lack acomplete or accurate medication list or medical history fortheir patient and, as a result, they can miss potentialcontraindications or duplicate therapies. Dispensers oftenhave difficulty reading handwritten prescriptions, andfrequently have little or no information about the patient’scondition for which the prescription is written. Contactingthe prescriber by phone to clarify what is ordered and to

make changes often results in delays for the patient, and itis time consuming for both the prescriber and the dispenser.There are disconnects between the prescriber and patient inthe medication process, with little or no feedback to theprescriber on whether a prescription was filled, or whatgeneric substitutions were made.

The e-Health Initiative describes how electronic pre-scribing is conducted in one of two ways, either via ahandheld device, such as a personal digital assistant (PDA),“smart phones” or though a web browser on a desktopapplication. Depending on the e-prescribing application thatis chosen by the physician’s practice, the patient’s demo-graphics might have been downloaded in the physician’sdatabase as part of the installation. When the prescriberstarts his/her day, information on patients who are sched-uled might be loaded in a queue for the prescriber to access.At this time, eligibility checks and medication history couldbe performed. When the prescriber is ready to prescribe, he/she could have at his/her disposal the patient’s formularyinformation and past medication history. When the pre-scriber writes a prescription for a patient he/she could bringup the patient file on the e-prescribing application. Fromthere the prescriber could search for the medication to beprescribed or could pick from a list of his/her mostcommonly prescribed medications. When a medication isselected, formulary and benefits (FB) and drug utilizationreview (DUR) could be performed. The e-prescribingsystem could warn the prescriber of any contraindicationsor alerts with the option to override it or choose anothermedication. Next, the prescriber could populate the quantityand directions fields, and the number of refills to completethe process. The prescriber could then send the electronicprescription to the patient’s pharmacy of choice. This entireprocess could take less than one minute to perform.

Because of e-prescribing’s likelihood of reducing medica-tion errors and costs, and enhancing patient safety, Congresslegislatively mandated that Medicare Part D plans support an“electronic prescription (e-prescribing) program.”

Statutory requirements

As further support for innovative use of e-prescribingmethods, Section 101 of the Medicare Prescription Drug,Improvement, and Modernization Act of 2003 (MMA)(Pub. L. 108–173) amended Title XVIII of the SocialSecurity Act (the Act) to establish the Voluntary Prescrip-tion Drug Benefit Program. Included in the provisions ofsection 1860D-4(e) of the Act is the requirement that theelectronic transmission of prescriptions and certain otherinformation for covered Part D drugs prescribed for Part Deligible individuals comply with standards adopted by theSecretary. Medicare Prescription Drug Plan (PDP) spon-

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sors, Medicare Advantage (MA) organizations offeringMedicare Advantage-Prescription Drug Plans (MA-PD)and other Part D sponsors are required to support andcomply with electronic prescribing standards once they arein effect, including any standards that were in effect whenthe drug benefit began in 2006.

There is no requirement that providers or pharmaciesimplement e-prescribing. However, providers and pharma-cies that electronically transmit prescription and certainother information for covered drugs prescribed for Medi-care Part D eligible beneficiaries are required to complywith any applicable final standards that are in effect. TheMMA requires the adoption of final standards to supportthe e-prescribing program described in the MMA. TheMMA mandates e-prescribing standards that allow forinformation exchange, to the extent feasible, on aninteractive, real-time basis; and allow for the exchange ofinformation only as it relates to the appropriate prescribingof drugs, including quality assurance measures and sys-tems. The MMA requires that standards for e-prescribing beconsistent with the objectives of improving patient safety,quality of care, and efficiencies.

Developing the standards that will facilitate e-prescribingis one of the key action items in the federal government’s planto build a nationwide electronic health information infrastruc-ture in the United States. E-prescribing has the potential todrive change in the healthcare industry. The unavailability ofdiagnostic testing and detection equipment outside of clinicalsettings makes expanded collection and use of informationproblematic.

Most solutions are provider-based, and limited byorganization-wide startup & maintenance costs, and risk-averse data distribution policies. A consumer-focusedapproach is needed. Objective, consumer-provided stan-dardized data can facilitate the use of distributed informa-tion networks in polypharmacy detection and avoidance.We propose here one promising model for polypharmacymanagement and integrated diagnostics through the use ofbreath-based, multiple array sensing and data capture.

Existing methods

As a potential application within industry initiatives, severalsystems have been examined which use a breath-baseddiagnostic device, including an array of multiple sensors aswell as a breath-based diagnostic method. Many healthdisorders are accompanied by elevated volatile organicchemical (VOC) levels in the patient’s breath due toabnormal metabolism. Diagnosis of such disorders cantherefore be achieved by breath test. However, disorder-related VOC presents in very low concentration in thebreath and therefore requires high sensitivity to detect.

Disorder-related VOC often contains more than onechemical and results from breath test are often complex,requiring extensive analysis. Gas chromatography-massspectrometer analysis has been used in breath test diagnosisin a number of applications. For example, systems by Phillips(1999) [6] and Katzman (2001) [7] propose such a methodfor medical diagnosis. However, gas chromatography-massspectrometer analysis requires a large amount of breath to beconcentrated into a suitable sample. Also, the data collectedfrom a gas chromatography-mass spectrometer needs to becompared with a spectrum to obtain a useful result.

One alternative approach to increase sensitivity to certainanalytes is to use selective filters or membranes. In aprocess proposed by De Castro et al., [8] an electrochemicalgas sensor is employed that has a catalytically active sensorelectrode, a reference electrode and a permselective filter ormembrane layer. The filter is made of a material thatprovides for molecular specificity of certain gases, such ascarbon monoxide, and the membrane allows the sensors tobe selective to the chemical analyte of interest. The filteronly allows the analytes of interest to contact the sensor. Byremoving interfering substances through filtration, thesensor becomes more selective and thus sensitive to theanalyte of interest.

As alternative systems, others have proposed methods fordetection of lung cancer, Breath ammonia testing fordiagnosis of hepatic encephalopathy, sulfide monitors, anddetection of toxic gases, such as ammonia, using a metaloxide semiconductor and an electrochemical sensor [9–13].

In view of the foregoing, one useful but often-overlooked technology set involves the application of vaporconcentrators within an array of sensors, especially relatedto electronic nose sensor arrays. In addition, methods arenow available to more discreetly detect odors and diagnosea range of medical conditions.

Proposed solution

Addressing the need to solve the aforementioned problemswhen using the gas chromatography-mass spectrometermethod, Lin (2004) [15] proposed that a timely datacollection system could be achieved by providing a deviceenabling diagnosis of disorder or physiological statedirectly from breath. This technology also provides adiagnostic method using the device. The diagnostic deviceis non-invasive and relatively quick, and the operation ofthe diagnostic device requires no complex training. More-over, using such a device, sample concentration and heatingprocesses are avoided and multiple chemicals can beanalyzed at the same time.

As shown in Fig. 1, a flow chart of disorder diagnosiscomprises the following steps. Breath to be tested is

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collected and allowed to react with a multi-sensor array. Asignal is generated from the reaction and sent to amicroprocessor. The signal is then compared with informa-tion in a database using identifying software to diagnose thephysiological state of the person. In Fig. 2, a structuraldiagram demonstrates one possible configuration. Thediagnostic device includes a sensory chamber and a signalprocessing unit. The processing unit includes an oscillator,a frequency counter, a digital/analog recorder, a micropro-cessor, and database storage device. A multi-sensor array isincluded, consisting of a piezoelectric quartz crystal, wherethe sensory device is established in a sensory chamber.When the breath to be tested is collected by a breath-collecting device and injected into the sensory chamber,specific VOC in the breath reacts with the sensory receptorcoated on the piezoelectric quartz crystal. An oscillationsignal is generated from the reaction. The oscillation signalis then transmitted to an oscillator coupled to a powersupply, counted in a frequency counter, and transmitted toan analog recorder to generate a digital data. Records arestored in the database storage device, and the digital datafrom a digital/analog recorder is received by microproces-sor and compared with the records from the databasestorage device to capture results of the diagnosis. A sensorychamber is cleaned by nitrogen or air injected by a micro-pump before the breath to be tested enters the breathcollecting device, and a flow meter is also set up forobserving flow of the breath, air, or nitrogen.

The diagnostic device utilizes a multi-sensor array todetect specific volatile organic chemicals (VOC) in thebreath, where the multi-sensor array carries a substance thatis reactive to the VOC as a sensory receptor. The datacollected is compared with a database for a variety ofdisorders to diagnose disorders or physiological state,creating a database built up by statistically analyzed datafor the disorders.

As a unit, the Lin device consists of a multi-sensor array,a database storage device and a microprocessor. The multi-sensor array comprises a series of sensory devices, eachreacting with specific VOC in breath to generate a signal.Each of the sensory devices reacts with the VOC directly orthrough a sensory receptor coating on the sensory device.The database contains information for a variety of disordersin the form of a pattern and/or digital record, and themicroprocessor receives the signal generated by the multi-sensor array and compares the signal with informationstored in the database to give a result.

Another aspect of this arrangement is a diagnosticmethod for disorder diagnosis. In this process, breath tobe tested is collected and allowed to react with a multi-sensor array. A signal is generated from the reaction andsent to a microprocessor, whereupon the signal is comparedwith information in a database to determine the physiolog-ical state of the person. The multi-sensor array includes aseries of sensory devices, each reacting with specific VOCin breath to generate a signal. Each of the sensory devicesreacts with the VOC directly or through a receptor coatingon the sensory device. The database contains informationfor a variety of disorders in the form of a pattern and/ordigital record. The microprocessor receives the signalgenerated by the multi-sensor array and compares thesignal with information stored in the database to give adiagnosis result.

The sensory device employed in the multi-array includesa quartz crystal sensory device, a metal oxide semiconduc-tor (MOS) sensory device, a surface acoustic wave (SAW)

Fig. 1 Flow chart of disorder diagnosis using breath reacted with amulti-sensor array. (Adapted from Lin, et.al., 2004)

Fig. 2 Sample configuration ofdiagnostic device using a sen-sory chamber and a signalprocessing unit. (Adapted fromLin, et.al., 2004)

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device, an electrode device, or a fiber optic device. MOSsensors and electrode devices are capable of reacting withspecific VOC themselves. Alternatively, a quartz crystalsensory device, SAW, and fiber optic devices need to becoated with an additional sensory receptor that reacts withspecific VOC in the breath. The quartz crystal sensorydevice can be, for example, a piezoelectric quartz crystalsensory device.

As illustration, Fig. 3 shows the normalized amplitudepatterns when the volatile vapor of trimethylamine, dime-thylamine, monomethylamine, ammonia, acetone, and for-mic acid are introduced to the piezoelectric quartz crystalmulti-sensor array device separately. Six piezoelectric quartzcrystals coated with A7, A8, A1, pb2, pb22 and cytc,respectively, are represented as peaks with different orienta-tions. As shown in Fig. 3, trimethylamine and ammonia havesimilar patterns, and dimethylamine and monomethylaminehave similar patterns. In contrast, formic acid and acetonehave different patterns and the signal is relatively small. Inthis case it is concluded that the diagnostic device responds

specifically and sensitively to trimethylamine, dimethyl-amine, monomethylamine, and ammonia.

As shown in Figs. 4 and 5, in a supporting clinical trialof this process, breath samples of normal subjects, uremiapatients, chronic kidney deficiency/chronic kidney failure(CRI/CRF) patients were collected from China MedicalCollege Hospital (Taichung, Taiwan). The uremia patentshad an average age of 51.6 (83 patients, age 29–80);chronic kidney deficiency/chronic kidney failure patientshad an average age of 65 (61 patients, age 45–83); normalsubjects had an average age of 32.8 (30 people, age 26–50).A 10 ml breath sample was used for the test. In a relatedtrial, breath samples of 31 normal subjects and 63 cirrhosispatients were collected. The samples were tested with thesix selected peptides using the Lin diagnostic device: Boththe device and method as described proved to have highaccuracy in the diagnosis of chronic kidney deficiency/chronic kidney failure and uremia.

In an optional configuration, the sensory receptor is aconductive macromolecule, organic compound, organelle,

Fig. 3 Normalized amplitude patterns of selected volatile vapors introduced to a piezoelectric quartz crystal multi-sensor array device. (Adaptedfrom Lin, et.al., 2004)

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peptide, protein, antibody, nucleic acid, metal oxide ormetal. The signal generated from the multi-array is a patternsignal or a digital signal. The pattern, for example, can be afingerprint, block chart, wave chart, or radio diagram.

The Lin device is suitable for the diagnosis of disorderssuch as uremia, cirrhosis, hyper methionine disorder,ketoacidosis, diabetes, periodontosis, gingivitis, lung can-cer, pulmonary abscess, schizophrenia, and intestinalobstruction. As an added feature, the device is suitable forthe diagnosis of disorders relating to abnormal metabolismor microorganism infectious disease [14, 15].

Implications and applications

The availability of a patient’s medication history can enableprescribers and pharmacists to prevent medical errors bychecking for redundant drugs and drug-drug interactions.Three pilot sites specifically tracked how frequently

prescribers accessed medication history information viathe e-prescribing system, or asked them how useful theyfound this information. Prescribers who used the medica-tion history function believed that it provided some benefit.In the SureScripts pilot site, there was variance in thefrequency with which medication history information wasused, with 53% of the 205 respondents at baseline statingthey reviewed medication history most of the time (45% of217 upon follow-up). Provider comments ranged fromthose who perceived the information as inaccurate, to afew providers who believed it was a good supplement to apatient’s “faulty” memory. At site visits where medicationhistory was discussed, there was poor integration of thisfunctionality into the e-prescribing workflow in some cases,and physician feedback suggests that they received verylittle education about the presence of this feature.

Breath-based e-prescribing has the potential to alertprescribers when they are prescribing a medication thatwould be inappropriate. Tests for appropriateness included

Fig. 4 Analysis chart of breathtest of normal subjects(Quadrature), uremia patients(Large circle), and chronickidney deficiency patients (X).(Adapted from Lin, et.al., 2004)

Fig. 5 Response distribution ofsensor array in the presenceof halothane. (Adapted fromLewis, et al., 2002)

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total number of medications, the Beers list of medicationsthat are generally inappropriate for the elderly, medicationsthat should be avoided in the presence of certain medicalconditions, and duplicative medications. This method alsohas the potential to reduce the number of adverse drugevents which in turn could reduce hospitalization andemergency department visits. This capability to check forthese alerts at the time of prescribing, which normallyhappens in the pharmacy setting, has been shown to reducepharmacy callbacks.

Despite growing industry consensus, efforts towards e-prescribing adoption have yielded limited results. Accord-ing to NCVHS testimony, in any given year physicianswrite over three billion prescriptions, and 65% of Ameri-cans take prescription drugs; however, according toindustry surveys results provided to the NCVHS, only 5%to 18% of physicians use e-prescribing. A primary reasoncited as to why fewer than 3% of all prescriptions arewritten with integrated e-prescribing systems is the lack ofe-prescribing standards. Moreover, NCVHS contends thatthe few standards that are available often are not publishedwith sufficient precision to be implemented in a way that istruly “standard.”

To realize the most significant benefits of e-prescribing,systems must be able to function across key steps in thedrug delivery chain—from writing prescriptions, to dis-pensing drugs, to payment. Currently, stakeholders in thischain have diverse interests and varying technologicalinfrastructures. Physician prescribers, pharmacy dispensersand the various Part D sponsors—Prescription Drug Plan(PDP) sponsors and Medicare Advantage (MA) organiza-tions offering Medicare Advantage-Prescription Drug (MA-PD) plans—must work together if integrated eprescribing isto become a reality.

Conclusion

A breath-based diagnostic device provides one possiblesolution to more timely population health data within ONCmandates. Such a model makes effective use of deviceswhich include an array of multiple gas sensors, a databasestorage device and a microprocessor. Existing gas sensortechnology utilizes material capable of reacting withvolatile organic chemicals in the exhaled breath of the

patient. The database storage device can store establishedresponses to a variety of diseases, where a microprocessorcan compare the response detected by the gas sensors andthe database in support of the medical diagnosis.

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